Učinkovitost aerosolne škatle za preprečevanje okužb pri posegih, pri katerih nastajajo aerosoli

Izpostavljenost okužbi zdravstvenih delavcev zaradi pandemije covida-19 je privedla do razvoja novih oblik osebne varovalne opreme. Ena od njih je aerosolna škatla, ki je bila zasnovana z idejo, kako varneje vstaviti dihalno cevko v sapnik bolnikom, okuženim z virusom SARS-CoV2. Uporaba aerosolne škatle ne sega samo v področje anesteziologije, ampak tudi v druga področja medicine. Številne kritike na račun prvotne zasnove so vodile v izdelavo novih oblik, ki postopoma izboljšujejo prvotne pomanjkljivosti. Razvoj se usmerja predvsem v okoliščino, kako zmanjšati oviranost kirurga (ali izvajalca) pri posegih ter kako zagotoviti varnejšo, enostavnejšo in hitrejšo možnost uporabe. Številnim avtorjem je uspelo pokazati, da uporaba aerosolne škatle pomembno vpliva na hitrost vstavitve dihalne cevke v sapnik, zato v urgentnih primerih odsvetujejo uporabo aerosolne škatle. Ob primerni uporabi ter ustreznem urjenju zdravstvenega delavca pa se je izkazala kot učinkovita osebna varovalna oprema, ker zdravstvene delavce dobro zaščiti pred velikimi in majhnimi vodnimi kapljicami. Pravilno posodobljena oblika aerosolne škatle dobro varuje tudi pred aerosolnimi delci, medtem ko uporaba izvorne oblike lahko tveganje za okužbo zdravstvenega delavca z aerosolnimi delci še poveča

Text mining MEDLINE to support public health

Today’s society is data rich and information driven, with access to numerous data sources available that have the potential to provide new insights into areas such as disease prevention, personalised medicine and data driven policy decisions. This paper describes and demonstrates the use of text mining tools developed to support public health institutions to complement their data with other accessible open data sources, optimize analysis and gain insight when examining policy. In particular we focus on the exploration of MEDLINE, the biggest structured open dataset of biomedical knowledge. In MEDLINE we utilize its terminology for indexing and cataloguing biomedical information – MeSH – to maximize the efficacy of the dataset

NewsMeSH: a new classifier designed to annotate health news with MeSH headings

Motivation In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. Methods We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. Results A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. Conclusions The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar

System Architecture of A European Platform for Health Policy Decision Making: MIDAS

Background: Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. Methods: The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. Results: A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. Conclusions: The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions

Meaningful Big Data Integration For a Global COVID-19 Strategy

Abstract With the rapid spread of the COVID-19 pandemic, the novel Meaningful Integration of Data Analytics and Services (MIDAS) platform quickly demonstrates its value, relevance and transferability to this new global crisis. The MIDAS platform enables the connection of a large number of isolated heterogeneous data sources, and combines rich datasets including open and social data, ingesting and preparing these for the application of analytics, monitoring and research tools. These platforms will assist public health author ities in: (i) better understanding the disease and its impact; (ii) monitoring the different aspects of the evolution of the pandemic across a diverse range of groups; (iii) contributing to improved resilience against the impacts of this global crisis; and (iv) enhancing preparedness for future public health emergencies. The model of governance and ethical review, incorporated and defined within MIDAS, also addresses the complex privacy and ethical issues that the developing pandemic has highlighted, allowing oversight and scrutiny of more and richer data sources by users of the system