This approach, together with other tools for reducing the risk of infection (surveillance, epidemiological guidelines, and training of healthcare personnel), could be applied to redesign and improve a wide range of healthcare processes.

BackgroundThroughout the world, emergency departments (ED) are characterized by overcrowding and excessive waiting times. Furthermore, the related delays significantly increase patient mortality and make inefficient use of resources to the detriment of the satisfaction of employees and patients. In this work, lean thinking is applied to the ED of Cardarelli Hospital of Naples with the aim of increasing patient flow, improving the processes that contribute to facilitating the flow of patients through the various stages of medical treatment and eliminating all bottlenecks (queue) as well as all activities that generate waste.MethodsThis project was performed at National Hospital A.O.R.N. A. Cardarelli of Naples. The historical times of access to the ED were analysed from January 2015 to June 2015, for a total of 16,563 records. Subsequently, starting in November 2015, corrective actions were implemented according to the Lean Approach. Data collected after the introduced improvements were collected from April 2016 to June 2016 and compared to those collected during the starting period.ResultsThe results acquired before application of the Lean Thinking strategy illustrated the as-is process with its drawbacks. An analysis of the non-added value activities was performed to identify the procedures that need to be improved. After implementation of the corrective actions, we observed a positive increase in the performance of the ED, quantified as percentages of hospitalized patients according to triage codes and waiting times.ConclusionThis work demonstrates the applicability of Lean Thinking to ED processes and its effectiveness in terms of increasing the efficiency of services and reducing waste (waiting times).

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PurposeThe recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.Design/methodology/approachIn this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.FindingsThe achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.Research limitations/implicationsA comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.Practical implicationsThe proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.Originality/valueThe proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of deaths related to birth, different challenges have emerged in identifying the main factors affecting mortality and morbidity. Dataset characterization: We investigated healthcare-associated infections in a cohort of 1203 patients at the level III Neonatal Intensive Care Unit (ICU) of the “Federico II” University Hospital in Naples from 2016 to 2020 (60 months). Methods: The present paper used statistical analyses and logistic regression to identify an association between healthcare-associated blood stream infection (HABSIs) and the available risk factors in neonates and prevent their spread. We designed a supervised approach to predict whether a patient suffered from HABSI using seven different artificial intelligence models. Results: We analyzed a cohort of 1203 patients and found that birthweight and central line catheterization days were the most important predictors of suffering from HABSI. Conclusions: Our statistical analyses showed that birthweight and central line catheterization days were significant predictors of suffering from HABSI. Patients suffering from HABSI had lower gestational age and birthweight, which led to longer hospitalization and umbilical and central line catheterization days than non-HABSI neonates. The predictive analysis achieved the highest Area Under Curve (AUC), accuracy and F1-macro score in the prediction of HABSIs using Logistic Regression (LR) and Multi-layer Perceptron (MLP) models, which better resolved the imbalanced dataset (65 infected and 1038 healthy).