Big data analytics in healthcare: a systematic literature review

The current study performs a systematic literature review (SLR) to synthesise prior research on the applicability of big data analytics (BDA) in healthcare. The SLR examines the outcomes of 41 studies, and presents them in a comprehensive framework. The findings from this study suggest that applications of BDA in healthcare can be observed from five perspectives, namely, health awareness among the general public, interactions among stakeholders in the healthcare ecosystem, hospital management practices, treatment of specific medical conditions, and technology in healthcare service delivery. This SLR recommends actionable future research agendas for scholars and valuable implications for theory and practice

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Big Data Analytics (BDA) has garnered significant attention in both academia and industries, particularly in sectors such as healthcare, owing to the exponential growth of data and advancements in technology. The integration of data from diverse sources and the utilization of advanced analytical techniques has the potential to revolutionize healthcare by improving diagnostic accuracy, enabling personalized medicine, and enhancing patient outcomes. In this paper, we aim to provide a comprehensive literature review on the application of big data analytics in healthcare, focusing on its ecosystem, applications, and data sources. To achieve this, an extensive analysis of scientific studies published between 2013 and 2023 was conducted and overall 180 scientific studies were thoroughly evaluated, establishing a strong foundation for future research and identifying collaboration opportunities in the healthcare domain. The study delves into various application areas of BDA in healthcare, highlights successful implementations, and explores their potential to enhance healthcare outcomes while reducing costs. Additionally, it outlines the challenges and limitations associated with BDA in healthcare, discusses modelling tools and techniques, showcases deployed solutions, and presents the advantages of BDA through various real-world use cases. Furthermore, this study identifies and discusses key open research challenges in the field of big data analytics in healthcare, aiming to push the boundaries and contribute to enhanced healthcare outcomes and decision-making processes

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining, and machine learning to healthcare engineering systems. A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest, and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors, and content. From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field. The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors’ previous knowledge and the nature of the publications were used to select different platforms. To the best of the authors’ knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining, and machine learning applied to healthcare engineering systems.N/

Big data and data repurposing – using existing data to answer new questions in vascular dementia research

Introduction: Traditional approaches to clinical research have, as yet, failed to provide effective treatments for vascular dementia (VaD). Novel approaches to collation and synthesis of data may allow for time and cost efficient hypothesis generating and testing. These approaches may have particular utility in helping us understand and treat a complex condition such as VaD. Methods: We present an overview of new uses for existing data to progress VaD research. The overview is the result of consultation with various stakeholders, focused literature review and learning from the group’s experience of successful approaches to data repurposing. In particular, we benefitted from the expert discussion and input of delegates at the 9th International Congress on Vascular Dementia (Ljubljana, 16-18th October 2015). Results: We agreed on key areas that could be of relevance to VaD research: systematic review of existing studies; individual patient level analyses of existing trials and cohorts and linking electronic health record data to other datasets. We illustrated each theme with a case-study of an existing project that has utilised this approach. Conclusions: There are many opportunities for the VaD research community to make better use of existing data. The volume of potentially available data is increasing and the opportunities for using these resources to progress the VaD research agenda are exciting. Of course, these approaches come with inherent limitations and biases, as bigger datasets are not necessarily better datasets and maintaining rigour and critical analysis will be key to optimising data use

Mapping Child Well-Being in Duval County, FL

Analyzes the distribution of neighborhood, education, and health/environmental opportunity; impact on health and education outcomes; demographics of those in Children's Commission programs; and marginalized neighborhoods' conditions. Outlines strategies

Emerging needs in behavioral health and the integrated care model

Medically vulnerable populations are constantly at risk of having poor health related outcomes, low satisfaction in the healthcare system and increased mortality. Studies have shown the increased prevalence rates of various medical comorbidities in patients with severe mental illness. These patients are obviously vulnerable because of their mental illness but they are also more likely to have severe cases of medical conditions commonly seen in the general population. Expenditures and utilization of resources is often inappropriate due to frequent visits for acute needs and low rates of preventative care and primary care appointments. My proposed model focuses on the implementation of the integrated care model which encourages collaboration between mental health professionals and primary care physicians through referral programs or integrated clinic settings. This model is initiated with education to both current clinicians as well as future clinicians through medical schools and residency programs. Once the education component has begun, the next steps are formal exploration, preparation, implementation and evaluation of the model in clinics. The aim is to improve health outcomes by increasing preventative care and using behavioral techniques to assist with adherence, increase satisfaction in the healthcare system and contain expenditures by utilizing primary care services instead of emergency services when appropriate

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