Search

Background: This work aims to identify policies implemented for healthy food environments in Italy within The Healthy Food Environment Policy Index (Food-EPI) project., Methods: Food-EPI tool, which includes two components, thirteen domains and fifty good practice indicators, was adapted for the Italian context. Evidence for implementation was gathered and summarized for all fifty indicators from data sources such as governmental websites, non-government organizations publications and websites and via direct contact with Government officials., Results: The highest level of evidence was found within five domains: food composition (2/2 indicators), labelling (3/4), promotion (4/5), provision (4/5) and leadership (5/5). The domains with less identified evidence were food prices (1/4), food in retail (0/4), trade and investment (0/2) and platforms and interaction (1/4)., Conclusions: The evidence summarization and the upcoming stakeholders' meeting to rate the level of implementation for each indicator in Italy have the potential to improve Government commitment to shape healthier food environments.

Objective: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring., Patients and Methods: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity., Results: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated., Conclusions: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.

A Surgical Suite (SS) is a complex system in which different healthcare professionals work. Inefficient management could lead to waste of money and time and reduce quality of care. The aim of the study was to carry out an organizational assessment of a SS in northern Italy, in order to identify weaknesses and inadequacies related to its performance and promote strategies to increase efficiency. The study was conducted by process mapping of the working context, qualitative and quantitative analysis of patient documents and an evaluation of the critical issues using the root cause analysis (RCA) tool. The Plan Do Check Act (PDCA) method was used to implement the necessary changes. A detailed description of the staff involved, medical devices available, organization and timing of the SS was performed. Inefficiencies in the unit were caused mainly by insufficient medical devices and underusage of the radiological software Picture Archiving and Communication System (PACS). Root causes of inefficiencies were identified and classified into four areas: organization/structure, personnel, technologies and methods. In particular, critical issues were identified in: the planning processes, the heterogeneity of technical and Information technology skills and educational background of nursing staff, the presence of several computerized information systems and lack of a connection interface between the different software, the lack of internal procedures and paths and lack of continuing professional education opportunities. Two multidisciplinary working tables were launched by the hospital management in order to identify improvement strategies. The evaluation allowed us to define the root causes of SS inefficiency in this hospital, leading to a reorganization with a view to continuous improvement. An innovative aspect of the present study was the use of RCA to perform an organizational assessment in healthcare, rather than as a reactive risk management tool.