Internet of Things and New Technologies for Tracking Perioperative Patients With an Innovative Model for Operating Room Scheduling: Protocol for a Development and Feasibility Study.

BACKGROUND: Management of operating rooms is a critical point in health care organizations because surgical departments represent a significant cost in hospital budgets. Therefore, it is increasingly important that there is effective planning of elective, emergency, and day surgery and optimization of both the human and physical resources available, always maintaining a high level of care and health treatment. This would lead to a reduction in patient waiting lists and better performance not only of surgical departments but also of the entire hospital. OBJECTIVE: This study aims to automatically collect data from a real surgical scenario to develop an integrated technological-organizational model that optimizes operating block resources. METHODS: Each patient is tracked and located in real time by wearing a bracelet sensor with a unique identifier. Exploiting the indoor location, the software architecture is able to collect the time spent for every step inside the surgical block. This method does not in any way affect the level of assistance that the patient receives and always protects their privacy; in fact, after expressing informed consent, each patient will be associated with an anonymous identification number. RESULTS: The preliminary results are promising, making the study feasible and functional. Times automatically recorded are much more precise than those collected by humans and reported in the organization's information system. In addition, machine learning can exploit the historical data collection to predict the surgery time required for each patient according to the patient's specific profile. Simulation can also be applied to reproduce the system's functioning, evaluate current performance, and identify strategies to improve the efficiency of the operating block. CONCLUSIONS: This functional approach improves short- and long-term surgical planning, facilitating interaction between the various professionals involved in the operating block, optimizing the management of available resources, and guaranteeing a high level of patient care in an increasingly efficient health care system. TRIAL REGISTRATION: ClinicalTrials.gov NCT05106621; https://clinicaltrials.gov/ct2/show/NCT05106621. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45477.

How did COVID-19 affect logistics and supply chain processes? Immediate, short and medium-term evidence from some industrial fields of Italy.

This paper provides empirical evidence on the impact of the Covid-19 pandemic on logistics and supply chain processes of five industrial sectors of Italy, namely food & beverage, machine manufacturing, metal mechanical industry, logistics & transport, and textile & fashion. A questionnaire survey, with 82 useful responses, was conducted to investigate various effects of Covid-19 on these businesses, such as the volumes handled and the service performance in the immediate-, short- and medium-term, the countermeasures implemented by companies and the future decision-making strategies. The period of analysis spans from January 2020 to June 2021. Results show that the impact of Covid-19 on volumes and service performance varied across the sectors: the food & beverage and logistics & transport were poorly affected by the pandemic and experienced a general increase in the demand and volumes, while mechanical or textile & fashion industries were mostly affected by a decrease in demand. The positive/negative impacts were particularly evident at the beginning of the pandemics, but, depending on the sector, the effects could cease quite quickly or last in the short-term. The countermeasures adopted against the Covid-19 emergency differ again across sectors; in general, industry fields that were particularly impacted by the pandemic emergency have applied more countermeasures. Typical strategies for risk management (e.g., the diversification in transport modes or the stock increase) turned out to be applied as immediate countermeasures or in plan for the future in few industries only. Differences across sectors were also observed about the sourcing strategies already in use, implemented to counteract the pandemics or expected to be maintained in time. Empirical outcomes offered are expected to help researchers gain a deep understanding of Covid-19 related phenomena, thus inspiring further research activities.

Low-Cost UWB Based Real-Time Locating System: Development, Lab Test, Industrial Implementation and Economic Assessment.

This paper presents the technical development and subsequent testing of a Real-Time Locating System based on Ultra-Wideband signals, with the aim to appraise its potential implementation in a real industrial case. The system relies on a commercial Radio Indoor Positioning System, called Qorvo MDEK1001, which makes use of UWB RF technology to determine the position of RF-tags placed on an item of interest, which in turn is located in an area covered by specific fixed antennas (anchors). Testing sessions were carried out both in an Italian laboratory and in a real industrial environment, to determine the best configurations according to some selected performance indicators. The results support the adoption of the proposed solution in industrial environments to track assets and work in progress. Moreover, most importantly, the solution developed is cheap in nature: indeed, normally tracking solutions involve a huge investment, quite often not affordable above all by small-, medium- and micro-sized enterprises. The proposed low-cost solution instead, as demonstrated by the economic assessment completing the work, justifies the feasibility of the investment. Hence, results of this paper ultimately constitute a guidance for those practitioners who intend to adopt a similar system in their business.

A literature review on quantitative models for supply chain risk management: Can they be applied to pandemic disruptions?

Supply chain risk management is considered a topic of increasing interest worldwide and its focus has evolved over time. The recent coronavirus pandemic (known as COVID-19) has forced business to handle a new global crisis and rapidly adapt to unexpected challenges. In an attempt to help companies counteract the pandemic risk, as well as to fuel the scientific discussion about this topic, this paper proposes a systematic literature review on risk management and disruptions in the supply chain focusing on quantitative models and paying a particular attention to highlighting the potentials of the studies reviewed for being applied to counteract pandemic emergencies. An appropriate query was made on Scopus and returned, after a manual screening, a useful set of 99 papers that proposed models for supply chain risk management. The relevant aspects of pandemics risk management have been first identified and mapped; then, the studies reviewed have been analysed with the aim of evaluating their suitability of being applied to sanitary crises. In carrying out this review of the literature, the study moves from previous, more general, reviews about risk management and updates them, starting from the lines of research that have been covered in recent years and evaluating their consistency with future research directions emerging also as a consequence of the pandemic crisis. Gaps and limitations of the existing models are identified and future research directions for pandemics risk management are suggested.

Integration of Digital Twin, Machine-Learning and Industry 4.0 Tools for Anomaly Detection: An Application to a Food Plant.

This work describes a structured solution that integrates digital twin models, machine-learning algorithms, and Industry 4.0 technologies (Internet of Things in particular) with the ultimate aim of detecting the presence of anomalies in the functioning of industrial systems. The proposed solution has been designed to be suitable for implementation in industrial plants not directly designed for Industry 4.0 applications. More precisely, this manuscript delineates an approach for implementing three machine-learning algorithms into a digital twin environment and then applying them to a real plant. This paper is based on two previous studies in which the digital twin environment was first developed for the industrial plant under investigation, and then used for monitoring selected plant parameters. Findings from the previous studies are exploited in this work and advanced by implementing and testing the machine-learning algorithms. The results show that two out of the three machine-learning algorithms are effective enough in predicting anomalies, thus suggesting their implementation for enhancing the safety of employees working at industrial plants.

Machine learning in perioperative medicine: a systematic review.

BACKGROUND: Risk stratification plays a central role in anesthetic evaluation. The use of Big Data and machine learning (ML) offers considerable advantages for collection and evaluation of large amounts of complex health-care data. We conducted a systematic review to understand the role of ML in the development of predictive post-surgical outcome models and risk stratification. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, we selected the period of the research for studies from 1 January 2015 up to 30 March 2021. A systematic search in Scopus, CINAHL, the Cochrane Library, PubMed, and MeSH databases was performed; the strings of research included different combinations of keywords: "risk prediction," "surgery," "machine learning," "intensive care unit (ICU)," and "anesthesia" "perioperative." We identified 36 eligible studies. This study evaluates the quality of reporting of prediction models using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist. RESULTS: The most considered outcomes were mortality risk, systemic complications (pulmonary, cardiovascular, acute kidney injury (AKI), etc.), ICU admission, anesthesiologic risk and prolonged length of hospital stay. Not all the study completely followed the TRIPOD checklist, but the quality was overall acceptable with 75% of studies (Rev #2, comm #minor issue) showing an adherence rate to TRIPOD more than 60%. The most frequently used algorithms were gradient boosting (n = 13), random forest (n = 10), logistic regression (LR; n = 7), artificial neural networks (ANNs; n = 6), and support vector machines (SVM; n = 6). Models with best performance were random forest and gradient boosting, with AUC > 0.90. CONCLUSIONS: The application of ML in medicine appears to have a great potential. From our analysis, depending on the input features considered and on the specific prediction task, ML algorithms seem effective in outcomes prediction more accurately than validated prognostic scores and traditional statistics. Thus, our review encourages the healthcare domain and artificial intelligence (AI) developers to adopt an interdisciplinary and systemic approach to evaluate the overall impact of AI on perioperative risk assessment and on further health care settings as well.

"Re-engineering of a food oven for thermal sanitization of Personal Protective Equipment against Sars-CoV-2 virus".

One of the main issues addressed by the recent COVID-19 pandemic which affected the whole world is the availability of Personal Protective Equipment (PPE) (e.g., face masks, white coats, or disposable gloves). This issue impacts on sustainability from different perspectives, such as more generated waste or environmental pollution, both for manufacturing and disposal, or more inequalities deriving from who can afford and access PPE and who cannot, since many shortages were recorded during the pandemic as well as fluctuating unit prices. Moreover, quite often PPE intended for single use are improperly used more times, thus generating a biological risk of infection. In an attempt to propose an innovative solution to face this problem, in this paper the re-design of an oven originally intended for food purposes is presented, with the aim of operating a thermal sanitization of PPE. The machinery and its components are detailed, together with physical and microbiological tests performed on non-woven PPE to assess the effect of treatment on mechanical properties and viral load. The pilot machinery turned out to be effective in destroying a bovine coronavirus at 95 °C and thus reducing contaminating risk in one hour without compromising the main properties of PPE, opening perspectives for the commercialization of the solution in the near future.

Environmental benefits of pet food obtained as a result of the valorisation of meat fraction derived from packaged food waste.

The 2030 Agenda of the United Nations includes the objective of setting up sustainable production patterns by pursuing several Sustainable Development Goals. Among them, the "Responsible production and consumption" is a key topic in the food production and is strictly connected with the "Climate action"; the crucial point, however, is how to jointly act on all these aspects and apply them in practice. The waste yearly produced in the food chain represent both an ethical, economic and environmental issue. In particular, as far as the recovery of packaged food waste from retailers is concerned, the valorisation of the wasted meat is an extremely relevant issue. Pet food industries could be interested in valorising this waste fraction to replace meat coming from slaughters in their product recipes. This article evaluates the environmental impact of valorising meat fraction from packaged food waste to produce two different recipes of high quality pet food, called Natura and Pâté. A life cycle assessment of the current scenario (traditional pet food production and landfilling of packaged food waste) and of a new one (pet food production using meat fraction from packaged food waste) is carried out applying the ReCiPe 2016 method of impact assessment. Real data have been taken from retailers and pet food manufacturer. The production of pet food using the meat fraction from packaged food waste generates on average lower environmental impacts if compared to the traditional process, in terms of GWP (-56.40%), water consumption (-22.62%), land use (-87.50%) and fossil resource scarcity (-21.78%). Benefits are interesting even if considering the production of Pâté (-14.66%), for which the traditional production process makes use of some slaughter by-products. The proposed industrial process is demonstrated to be sustainable from an environmental point of view and appears to be in line with Sustainable Development Goals (SDGs) 2, 12 and 13.