Update and Acceleration of Health Care Using Artificial Intelligence in Medical Treatments and Diagnostics

Researchers support the growth of artificial intelligence and similar methods in health and medical care for the purpose of continuously improving processes. By focusing on the growth on data analytics, statistics, applied mathematics, and computer methods including machine learning, the future of health-care methods will change. The development of computerized methods and the growth of data systems produce ample materials for artificial intelligence to develop and to bring physician assistance programs to enable continuous improvement resulting in superior health and medical care. This includes applications in intensive care as well as diagnostic therapies. The focus is on examples in the use of the promising developments in data science methods, the accumulation of medical and research data. With quality and continuous improvement in process control applications where one determines the usefulness of data analytics, there are great possibilities of change in the improvement in medical applications as well as the management of medical and health-care treatment and diagnostic facilities.

Systematic review of performance-enhancing health worker supervision approaches in low- and middle-income countries

Background The strength of a health system—and ultimately the health of a population—depends to a large degree on health worker performance. However, insufficient support to build, manage and optimize human resources for health (HRH) in low- and middle-income countries (LMICs) results in inadequate health workforce performance, perpetuating health inequities and low-quality health services. Methods The USAID-funded Human Resources for Health in 2030 Program (HRH2030) conducted a systematic review of studies documenting supervision enhancements and approaches that improved health worker performance to highlight components associated with these interventions’ effectiveness. Structured by a conceptual framework to classify the inputs, processes, and results, the review assessed 57 supervision studies since 2010 in approximately 29 LMICs. Results Of the successful supervision approaches described in the 57 studies reviewed, 44 were externally funded pilots, which is a limitation. Thirty focused on community health worker (CHW) programs. Health worker supervision was informed by health system data for 38 approaches (67%) and 22 approaches used continuous quality improvement (QI) (39%). Many successful approaches integrated digital supervision technologies (e.g., SmartPhones, mHealth applications) to support existing data systems and complement other health system activities. Few studies were adapted, scaled, or sustained, limiting reports of cost-effectiveness or impact. Conclusion Building on results from the review, to increase health worker supervision effectiveness we recommend to: integrate evidence-based, QI tools and processes; integrate digital supervision data into supervision processes; increase use of health system information and performance data when planning supervision visits to prioritize lowest-performing areas; scale and replicate successful models across service delivery areas and geographies; expand and institutionalize supervision to reach, prepare, protect, and support frontline health workers, especially during health emergencies; transition and sustain supervision efforts with domestic human and financial resources, including communities, for holistic workforce support. In conclusion, effective health worker supervision is informed by health system data, uses continuous quality improvement (QI), and employs digital technologies integrated into other health system activities and existing data systems to enable a whole system approach. Effective supervision enhancements and innovations should be better integrated, scaled, and sustained within existing systems to improve access to quality health care.

Human-Computer Interaction With Big Data Analytics

Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics.

Big Data, Data Mining, and Data Analytics in IoT-Based Healthcare Applications

IoT seriously impacts every industry. The healthcare industry has experienced progression in digitizing medical records. Healthcare services are costlier than ever. Data mining is one of the largest challenges to face IoT. Big Data is an accumulation of data. IoT devices receive lots of data. Big data systems can do a lot of data analytics. The tools can also be used to perform these operations. The big health application system can be built by integrating medical health resources using intelligent terminals, internet of things (IoT), big data, and cloud computing. People suffer from many diseases. A big health system can be applied to scientific health management by detecting risk factors for the occurrence of diseases. Patients can have special attention to their health requirements and their devices can be tuned to remind them of their appointments, calorie count, exercise check, blood pressure variations, symptoms of any diseases, and so much more.

Big Data Analytics in Supply Chain Management

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

Restart tourism | build the future

PurposeThis viewpoint aims to explore the question: How can we restart and monitor the path towards the tourism of the future?Design/methodology/approachThis paper identifies the progress made at scientific, institutional, political and technological levels, and how it is possible to foresee that we will enter in a new era of tourism indicators.FindingsA significant body of literature clearly demonstrates that tourism cannot be viewed simply from an economic point of view as it has a great influence on sociocultural and environmental dimensions. The impact of tourism and how to ensure its long-term success has been invoked for the last few decades, leading to the direct consideration of sustainability indicators in a wide array of scientific publications. However, despite significant advances, the lack of funding, lack of support or interest from the political community, bureaucracies or lack of methodological guidance and of technical skills along the entire value chain pose clear challenges to the development and adoption of wide data systems to support sustainable tourism policies.Originality/valueThe paper sheds light on the Portuguese position regarding the recovery of the tourism sector in the aftermath of the COVID-19 pandemic. It also highlights the commitment to knowledge and monitoring of sustainability in tourism, articulated at international level, and how this is essential in order to make progress and to overcome the challenges facing the sector. At the same time, it demonstrates how fundamental it is to identify solutions to boost the potential of tourism as an economic, social, environmental and cultural phenomenon.

Trends in drowning mortality in Portugal from 1992 to 2019: comparing Global Burden of Disease and national data

IntroductionImprecise data systems hinder understanding of drowning burden, even in high-income countries like Portugal, that have a well-implemented death certificate system. Consequently, national studies on drowning mortality are scarce. We aimed to explore drowning mortality in Portugal using national data and to compare these to Global Burden of Disease (GBD) estimates.MethodsData were obtained from the National Institute of Statistics (INE) for 1992–2019, using International Classification of Diseases (ICD)-9 and ICD-10 codes, by sex, age group and cause (unintentional; water transport and intentional). GBD unintentional drowning data were obtained online. Age-standardised drowning rates were calculated and compared.ResultsINE data showed 6057 drowning deaths, 4327 classified as unintentional (75.2% male; 36.7% 35–64 years; 31.5% 65+years; 15.2% 0–19 years). Following 2001, an increase in accidental drowning mortality and corresponding decrease in undetermined intent was observed, coincident with Portugal’s ICD-10 implementation. GBD modelled estimates followed a downward trend at an overall rate of decrease of −0.41/decade (95% CI (−0.45 to –0.37); R2adj=0.94; p<0.05). Conversely, INE data showed an increase in the rate of drowning deaths over the last decade (0.35/decade; 95% CI (−0.18 to 0.89)). GBD estimates were significantly different from the INE dataset (alpha=0.05), either underestimating as much as 0.567*INE in 1996 or overestimating as much as 1.473*INE in 2011.ConclusionsWhile GBD mortality data estimates are valuable in the absence of routinely collected data, they smooth variations, concealing key advocacy opportunities. Investment in country-level drowning registries enables in-depth analysis of incident circumstances. Such data are essential to informing National Water Safety Plans.