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The OHIO (Odin Hospital Indoor cOmpass) project received funding from the European Union's Horizon 2020 research and innovation action program, via ODIN–Open Call, which is issued and executed under the ODIN project and focuses on enhancing hospital safety, productivity, and quality by introducing digital solutions, such as the Internet of Things (IoT), robotics, and artificial intelligence (AI). OHIO aims to enhance the productivity and quality of medical equipment maintenance activities within the pilot hospital, "Le Scotte" in Siena (Italy), by leveraging internal informational resources. OHIO will also be completely integrated with the ODIN platform, taking advantage of the available services and functionalities. OHIO exploits Bluetooth Low Energy (BLE) tags and antennas together with the resources provided by the ODIN platform to develop a complex ontology-based IoT framework, which acts as a central cockpit for the maintenance of medical equipment through a central management web application and an indoor real-time location system (RTLS) for mobile devices. The application programmable interfaces (APIs) are based on REST architecture for seamless data exchange and integration with the hospital's existing computer-aided facility management (CAFM) and computerized maintenance management system (CMMS) software. The outcomes of the project are assessed both with quantitative and qualitative methods, by evaluating key performance indicators (KPIs) extracted from the literature and performing a preliminary usability test on both the whole system and the graphic user interfaces (GUIs) of the developed applications. The test implementation demonstrates improvements in maintenance timings, including a reduction in maintenance operation delays, duration of maintenance tasks, and equipment downtime. Usability post-test questionnaires show positive feedback regarding the usability and effectiveness of the applications. The OHIO framework enhanced the effectiveness of medical equipment maintenance by integrating existing software with newly designed, enhanced interfaces. The research also indicates possibilities for scaling up the developed methods and applications to additional large-scale pilot hospitals within the ODIN network. [ABSTRACT FROM AUTHOR]

Acute healthcare providers operate large, diverse medical equipment inventories. Resources for managing these inventories is frequently scarce so must be prioritised such that maximum benefit is conferred per unit of expenditure.This review identifies publications which have discussed the clinical value conferred by mechanical ventilation (MV) and by extra corporeal membrane oxygenation (ECMO). Respectively, mechanical ventilators and ECMO units are necessary to deliver these therapies. Systematic searches for publications which discuss the clinical value conferred by MV and by ECMO were conducted.The identified articles included reviews, prospective studies, retrospective studies, and models. Most presented findings in terms of the cost-effectiveness ratio. The patient populations studied, and analytical methods used varied widely. The clinical value conferred by MV varied with dependencies on several factors including the age- and disease- profile of the patient population. It was not possible to infer these dependencies from the literature which exists for ECMO.More relevant literature existed for MV, the more mature technology, than did for ECMO. The ECMO literature also tended to be more recent and included more modelling studies and fewer prospective studies. The data extracted could inform estimates of the clinical value likely to be delivered by mechanical ventilators operated by a specific institution. Estimates for ECMO are likely to carry greater uncertainty than those for MV.

The increasing use of electromedical equipment in hospital care services necessitates effective management of complex devices often unsupported by existing control systems. This paper focuses on developing a pool of evaluation indices for the Clinical Engineering Department (CED) of the ASST Grande Ospedale Metropolitano Niguarda in Milano (Italy), aiming to enhance awareness of the economic value, assess operational units, and optimize maintenance processes. Leveraging business intelligence, this study identifies 18 key performance indicators (KPIs) across logistics, technical, and equipment management categories. An interactive dashboard, implemented using Power BI, facilitates dynamic analysis and visualization of these KPIs, providing insights into the maintenance efficiency and obsolescence of medical devices. It offers a comprehensive framework for ongoing monitoring and decision-making. The results showcase the potential of the developed KPIs and dashboard to enhance operational insights and guide improvements in the healthcare facility's maintenance processes. [ABSTRACT FROM AUTHOR]

The OHIO (Odin Hospital Indoor cOmpass) project received funding from the European Union’s Horizon 2020 research and innovation action program, via ODIN–Open Call, which is issued and executed under the ODIN project and focuses on enhancing hospital safety, productivity, and quality by introducing digital solutions, such as the Internet of Things (IoT), robotics, and artificial intelligence (AI). OHIO aims to enhance the productivity and quality of medical equipment maintenance activities within the pilot hospital, “Le Scotte” in Siena (Italy), by leveraging internal informational resources. OHIO will also be completely integrated with the ODIN platform, taking advantage of the available services and functionalities. OHIO exploits Bluetooth Low Energy (BLE) tags and antennas together with the resources provided by the ODIN platform to develop a complex ontology-based IoT framework, which acts as a central cockpit for the maintenance of medical equipment through a central management web application and an indoor real-time location system (RTLS) for mobile devices. The application programmable interfaces (APIs) are based on REST architecture for seamless data exchange and integration with the hospital’s existing computer-aided facility management (CAFM) and computerized maintenance management system (CMMS) software. The outcomes of the project are assessed both with quantitative and qualitative methods, by evaluating key performance indicators (KPIs) extracted from the literature and performing a preliminary usability test on both the whole system and the graphic user interfaces (GUIs) of the developed applications. The test implementation demonstrates improvements in maintenance timings, including a reduction in maintenance operation delays, duration of maintenance tasks, and equipment downtime. Usability post-test questionnaires show positive feedback regarding the usability and effectiveness of the applications. The OHIO framework enhanced the effectiveness of medical equipment maintenance by integrating existing software with newly designed, enhanced interfaces. The research also indicates possibilities for scaling up the developed methods and applications to additional large-scale pilot hospitals within the ODIN network.

The primary goal of this project is to create a framework to extract Real-World Evidence to support Health Technology Assessment, Health Technology Management, Evidence-Based Maintenance, and Post Market Surveillance (as outlined in the EU Medical Device Regulation 2017/745) of medical devices using Natural Language Processing (NLP) and Artificial Intelligence. An initial literature review on Spontaneous Reporting System databases, Health Information Technologies (HIT) fault classification, and Natural Language Processing has been conducted, from which it clearly emerges that adverse events related to HIT are increasing over time. The proposed framework uses NLP techniques and Explainable Artificial Intelligence models to automatically identify HIT-related adverse event reports. The designed model employs a pre-trained version of ClinicalBERT that has been fine-tuned and tested on 3,075 adverse event reports extracted from the FDA MAUDE database and manually labelled by experts.

Purpose: Effective health technology management (HTM) is critical to healthcare delivery worldwide. The absence of proper HTM in low-resource settings leads to a lack of medical equipment that is operational or safe for use. Previous solutions including medical equipment donation have proven ineffective as The World Health Organization estimates that 50–80% of equipment is non-functional. This paper examines the facilitators and barriers to implementing Computerized Maintenance Management Systems (CMMS) as a means of improving HTM in Low-Middle-Income Countries (LMIC). Methods: Five electronic databases were searched. Original research, literature reviews, and systematic reviews in English were included. Abstracts from all eligible articles were reviewed and, if indicated, the full text was screened. Furthermore, the reference list of included articles was scanned for relevant studies. Articles were analyzed using a 'constant comparative' method. Results: Themes identified include quality improvement, facilitators to CMMS implementation, and barriers to CMMS implementation. Hospitals in resource-limited settings report improvements in their maintenance practices and operational costs following the implementation of CMMS. A centralized internet-accessible server and standardization of training were found to be the main facilitators of CMMS implementation. Major barriers included difficulty retaining trained of trained engineers, the need for continued education, costs, the lack of consumables needed for equipment maintenance, and considerations of local context. Conclusion: The importance of proper HTM in resource-limited hospital settings cannot be overlooked. The absence of proper HTM can be, in part, attributed to the shortage of skilled engineers. This emphasizes the need for access to continued biomedical engineering education to continue to advance and develop HTM in low resource settings. Although the implementation of CMMS in LMICs presents a multitude of challenges, our review found that it is by no means unfeasible, especially when adequate standardization and local context are taken into consideration. [ABSTRACT FROM AUTHOR]

Purpose: Hospital facilities and social life, along with the global economy, have been severely challenged by COVID-19 since the World Health Organization (WHO) declared it a pandemic in March 2020. Since then, countless ordinary citizens, as well as healthcare workers, have contracted the virus by just coming into contact with infected surfaces. In order to minimise the risk of getting infected by contact with such surfaces, our study aims to design, prototype, and test a new device able to connect users, such as common citizens, doctors or paramedics, with either common-use interfaces (e.g., lift and snack machine keyboards, traffic light push-buttons) or medical-use interfaces (e.g., any medical equipment keypad) Method: To this purpose, the device was designed with the help of Unified Modelling Language (UML) schemes, and was informed by a risk analysis, that highlighted some of its essential requirements and specifications. Consequently, the chosen constructive solution of the robotic system, i.e., a robotic-arm structure, was designed and manufactured using computer-aided design and 3D printing. Result: The final prototype included a properly programmed micro-controller, linked via Bluetooth to a multi-platform mobile phone app, which represents the user interface. The system was then successfully tested on different physical keypads and touch screens. Better performance of the system can be foreseen by introducing improvements in the industrial production phase. Conclusion: This first prototype paves the way for further research in this area, allowing for better management and preparedness of next pandemic emergencies. [ABSTRACT FROM AUTHOR]

With the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. Respiratory equipment plays a vital role in modern healthcare, supporting patients with compromised or impaired respiratory capacities. However, ensuring the reliability and safety of these devices is crucial to prevent adverse events and ensure patient well-being.This study aims to explore machine learning techniques to enhance predictive maintenance for mechanical ventilators.The dataset used for this study contains information about 1350 entries of mechanical ventilators, made by 15 different manufacturers and available in 30 distinct models. Different machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, K-nearest Neighbors, Support Vector Machines, Naive Bayes, and XG Boost are developed and tested in terms of their performance in predicting mechanical ventilator failures.The ensemble methods, particularly Random Forest and XGBoost, have proven to be more adept at handling the complexities of the dataset. The Decision Tree and Random Forest models both showed remarkable accuracies of approximately 0.993, while K-Nearest Neighbors (KNN) performed exceptionally with near perfect accuracy.Adoption of automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of MDs that are already being used in healthcare institutions. Implementing machine learning-based predictive maintenance can significantly enhance the reliability of mechanical ventilators in healthcare settings. [ABSTRACT FROM AUTHOR]

Analysis of data from incident registries such as MAUDE has identified the need to improve surveillance and maintenance strategies for infusion pumps to enhance patient and healthcare staff safety.The ultimate goal is to enhance infusion pump management strategies in healthcare facilities, thus transforming the current reactive approach to infusion pump management into a proactive and predictive one.This study utilized real data collected from 2015 to 2021 through the inspection of infusion pumps in Bosnia and Herzegovina. Inspections were conducted by the national laboratory in accordance with the Legal Metrology Framework, accredited to ISO 17020 standard. Out of 988 samples, 790 were used for model training, while 198 samples were set aside for validation (20% of the dataset). Various machine learning algorithms for binary classification of samples (pass/fail status) were considered, including Logistic Regression, Decision Tree, Random Forest, Naive Bayes, and Support Vector Machine. These algorithms were chosen for their ability to handle large datasets and potential for high prediction accuracy.Through detailed analysis of the achieved results, it was found that all applied machine learning methods yielded satisfactory results, with accuracy ranging from 0.98% to 1.0%, precision from 0.99% to 1%, sensitivity from 0.98% to 1.0%, and specificity from 0.87% to 1.0%. However, Decision Tree and Random Forest methods proved to be the best, both due to their maximum achieved values of accuracy, precision, sensitivity, and specificity, and due to result interpretability.It has been established that machine learning methods are capable of identifying potential issues before they become critical, thus playing a crucial role in predicting the performance of infusion pumps, potentially enhancing the safety, reliability, and efficiency of healthcare delivery. Further research is needed to explore the potential application of machine learning algorithms in various healthcare domains and to address practical issues related to the implementation of these algorithms in real clinical settings. [ABSTRACT FROM AUTHOR]

IntroductionDigital transformation and technological innovation which have influenced several areas of social and productive life in recent years, are now also a tangible and concrete reality in the vast and strategic sector of public healthcare. The progressive introduction of digital technologies and their widespread diffusion in many segments of the population undoubtedly represent a driving force both for the evolution of care delivery methods and for the introduction of new organizational and management methods within clinical structures.MethodsThe CS Clinical Engineering of the “Spedali Civili Hospital in Brescia” decided to design a path that would lead to the development of a software for the management of biomedical technologies within its competence inside the hospital. The ultimate aim of this path stems from the need of Clinical Engineering Department to have up-to-date, realistic, and systematic control of all biomedical technologies present in the company. “Spedali Civili Hospital in Brescia” is not just one of the most important corporate realities in the city, but it is also the largest hospital in Lombardy and one of the largest in Italy. System development has followed the well-established phases: requirement analysis phase, development phase, release phase and evaluating and updating phase.ResultsFinally, cooperation between the various figures involved in the multidisciplinary working group led to the development of an innovative management software called “SIC Brescia”.DiscussionThe contribution of the present paper is to illustrate the development of a complex implementation model for the digitization of processes, information relating to biomedical technologies and their management throughout the entire life cycle. The purpose of sharing this path is to highlight the methodologies followed for its realization, the results obtained and possible future developments. This may enable other realities in the healthcare context to undertake the same type of pathway inspired by an accomplished model. Furthermore, future implementation and data collection related to the proposed Key Performance Indicators, as well as the consequent development of new operational management models for biomedical technologies and maintenance processes will be possible. In this way, the Clinical Risk Management concept will also be able to evolve into a more controlled, safe, and efficient system for the patient and the user.

Objective: Case reports, tissue pathology, and autopsies have suggested that the hydrophilic polymer coating designed to improve endovascular deliverability and minimize vessel trauma can embolize and be associated with adverse outcomes such as ischemia, infarction, and death. This study sought to determine whether hydrophilic polymers shed off commercially available sheaths in a controlled in vitro environment, with the hypothesis that significant differences between coated and uncoated (control) sheaths would be found. Methods: Six sheaths from each manufacturer, including Zenith Alpha abdominal endovascular stent grafts (Cook Medical), DrySeal sheaths (W.L. Gore & Associates), and Sentrant Introducer sheaths (Medtronic), were tested in an in vitro environment. Noncoated Check-Flo performer introducer sheaths (Cook Medical) were used as controls. Each test circuit ran for 150 minutes at an output of 3 L/min, the circuit was then drained and the fluid collected. Quantitative analysis included weighing the dried filter paper and using particle size light scattering to quantify the particle size and count. Attenuated total reflectance spectroscopy was also used. Results: Each of the three coated sheaths had significantly greater shedding compared with the control sheaths. The Cook Zenith alpha sheath had significantly more residue weight (2.87 ± 0.52 mg/L) than the Gore DrySeal (1.07 ± 0.06 mg/L) and Medtronic Sentrant introducer (0.98 ± 0.14 mg/L) sheaths. The average particle size was not significantly different between the coated and uncoated (control) sheaths. Attenuated total reflectance spectroscopy identified sheath particulate in the Cook Zenith Alpha and Medtronic Sentrant samples. Conclusions: Polymer embolization was present and significantly greater in all three commercially available hydrophilic sheaths compared with the control group. Further investigation is needed into the clinical significance of these findings. : Clinical Relevance: Hydrophilic polymer coatings have significantly enhanced patient outcomes after endovascular procedures. However, isolated case reports have raised concerns about the potential embolization of such coatings, leading to end-organ ischemia and damage. Our results confirm the presence of this phenomenon. This knowledge will enable patients, physicians, and manufactures to make informed decisions and take appropriate precautions. Furthermore, it is imperative to conduct further research to thoroughly characterize the embolization profile of different sheaths. Such investigations would contribute to our understanding of the problem and provide valuable information for manufactures to guide the development of safer and more reliable devices.

Acute healthcare providers operate large, diverse medical equipment inventories. Resources for managing these inventories is frequently scarce so must be prioritised such that maximum benefit is conferred per unit of expenditure. This review identifies publications which have discussed the clinical value conferred by mechanical ventilation (MV) and by extra corporeal membrane oxygenation (ECMO). Respectively, mechanical ventilators and ECMO units are necessary to deliver these therapies. Systematic searches for publications which discuss the clinical value conferred by MV and by ECMO were conducted. The identified articles included reviews, prospective studies, retrospective studies, and models. Most presented findings in terms of the cost-effectiveness ratio. The patient populations studied, and analytical methods used varied widely. The clinical value conferred by MV varied with dependencies on several factors including the age- and disease- profile of the patient population. It was not possible to infer these dependencies from the literature which exists for ECMO. More relevant literature existed for MV, the more mature technology, than did for ECMO. The ECMO literature also tended to be more recent and included more modelling studies and fewer prospective studies. The data extracted could inform estimates of the clinical value likely to be delivered by mechanical ventilators operated by a specific institution. Estimates for ECMO are likely to carry greater uncertainty than those for MV., Competing Interests: None declared., (© 2024 The Authors. Published by Elsevier Ltd on behalf of Institute of Physics and Engineering in Medicine (IPEM).)