Digitalization of healthcare: Russian and foreign specifics

Currently, we are witnessing rapid changes of the modern economic system through the introduction of various digital technologies. The healthcare sector is no exception, but rather the digitization of the industry, thereby optimizing the provision of health services, increase quality control and reduce costs. The article describes the informatization process of the health care industry in the world and the Russian Federation. Currently, in the context of contemporary processes of digital transformation is modernization of the health care system's main stimulating technological progress is the use of medical information systems (MIS), introduction of medical products of the Internet of things (IoMT), advanced big data Analytics (Big Data) and the practical application of medical expert systems. In the conclusion the basic conclusions on results of the informatization in the sphere of healthcare in the Russian Federation on Federal and regional levels

Digitalization of healthcare: Russian and foreign specifics

Currently, we are witnessing rapid changes of the modern economic system through the introduction of various digital technologies. The healthcare sector is no exception, but rather the digitization of the industry, thereby optimizing the provision of health services, increase quality control and reduce costs. The article describes the informatization process of the health care industry in the world and the Russian Federation. Currently, in the context of contemporary processes of digital transformation is modernization of the health care system's main stimulating technological progress is the use of medical information systems (MIS), introduction of medical products of the Internet of things (IoMT), advanced big data Analytics (Big Data) and the practical application of medical expert systems. In the conclusion the basic conclusions on results of the informatization in the sphere of healthcare in the Russian Federation on Federal and regional levels

Multi-Label Chest X-Ray Classification via Deep Learning

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research

Technologie en innovatie in Vlaanderen: Prioriteiten.

Vlaanderen;

Simulation in manufacturing and business: A review

Copyright @ 2009 Elsevier B.V.This paper reports the results of a review of simulation applications published within peer-reviewed literature between 1997 and 2006 to provide an up-to-date picture of the role of simulation techniques within manufacturing and business. The review is characterised by three factors: wide coverage, broad scope of the simulation techniques, and a focus on real-world applications. A structured methodology was followed to narrow down the search from around 20,000 papers to 281. Results include interesting trends and patterns. For instance, although discrete event simulation is the most popular technique, it has lower stakeholder engagement than other techniques, such as system dynamics or gaming. This is highly correlated with modelling lead time and purpose. Considering application areas, modelling is mostly used in scheduling. Finally, this review shows an increasing interest in hybrid modelling as an approach to cope with complex enterprise-wide systems

Strengthening strategic management approaches to address antimicrobial resistance in global human health: a scoping review

Introduction The development and implementation of national strategic plans is a critical component towards successfully addressing antimicrobial resistance (AMR). This study aimed to review the scope and analytical depth of situation analyses conducted to address AMR in human health to inform the development and implementation of national strategic plans. Methods A systematic search of the literature was conducted to identify all studies since 2000, that have employed a situation analysis to address AMR. The included studies are analysed against frameworks for strategic analysis, primarily the PESTELI (Political, Economic, Sociological, Technological, Ecological, Legislative, Industry) framework, to understand the depth, scope and utility of current published approaches. Results 10 studies were included in the final review ranging from single country (6) to regional-level multicountry studies (4). 8 studies carried out documentary review, and 3 of these also included stakeholder interviews. 2 studies were based on expert opinion with no data collection. No study employed the PESTELI framework. Most studies (9) included analysis of the political domain and 1 study included 6 domains of the framework. Technological and industry analyses is a notable gap. Facilitators and inhibitors within the political and legislative domains were the most frequently reported. No facilitators were reported in the economic or industry domains but featured inhibiting factors including: lack of ring-fenced funding for surveillance, perverse financial incentives, cost-shifting to patients; joint-stock drug company ownership complicating regulations. Conclusion The PESTELI framework provides further opportunities to combat AMR using a systematic, strategic management approach, rather than a retrospective view. Future analysis of existing quantitative data with interviews of key strategic and operational stakeholders is needed to provide critical insights about where implementation efforts should be focussed, and also how to build contingency at the strategic level for agile responses to macro-level environmental influences

Data analytics based positioning of health informatics programs

The Master of Science in Computer Information Systems (CIS) with concentration in Health Informatics (HI) at Metropolitan College (MET), Boston University (BU), is a 40-credit degree program that are delivered in three formats: face-to-face, online, and blended. The MET CIS-HI program is unique because of the population of students it serves, namely those interested in gaining skills in HI technology field, to serve as data analysts and knowledge-based technology drivers in the thriving health care industry. This set of skills is essential for addressing the challenges of Big Data and knowledge-based health care support of the modern health care. The MET CIS-HI program was accredited by the Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM) in 2017

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution