Big Data Analytics In Healthcare Research Articles

To Study Opportunities and Challenges of Big Data Analytics in Healthcare

Big data analytics in healthcare is a powerful tool that can bring about significant improvements in patient care, operational efficiency, and medical research. This study explores the various aspects of big data analytics in healthcare, aiming to uncover its many opportunities and challenges. Through a thorough review of existing literature and real-world case studies, this research highlights the potential benefits of big data analytics, such as better disease prediction, personalized treatment approaches, and optimized resource allocation. However, there are also notable challenges to overcome, including concerns about data privacy, problems with sharing information between different systems, and the need for skilled staff and strong infrastructure. By combining insights from different perspectives, this study provides a complete understanding of the current state of big data analytics in healthcare, offering valuable information for policymakers, healthcare professionals, and researchers to navigate this evolving field effectively.

Leveraging Snomed Ct Within Standardized E-Clinical Pathways: A Key For Extensive Healthcare Big Data Analytics

Leveraging Snomed Ct Within Standardized E-Clinical Pathways: A Key For Extensive Healthcare Big Data Analytics

Modelling of healthcare data analytics using optimal machine learning model in big data environment

AbstractRecent advances in wireless networking, big data technologies, namely Internet of Things (IoT) 5G networks, health care big data analytics, and other technologies in artificial intelligence (AI) and wearables, have supported the progression of intellectual disease diagnosis methods. Medical data covers all patient data such as pharmacy texts, electronic health reports (EHR), prescriptions, study data from medical journals, clinical photographs, and diagnostic reports. Big data is a renowned method in the healthcare sector, with beneficial datasets that are highly difficult, voluminous, and rapid for healthcare providers for interpreting and computing using prevailing tools. This study combines concepts like deep learning (DL) and big data analytics in medical field. This article develops a new healthcare data analytics using optimal machine learning model in big data environment (HDAOML‐BDE) technique. The presented HDAOML‐BDE technique mainly aims to examine the healthcare data for disease detection and classification in the big data environment. For handling big data, the HDAOML‐BDE technique uses Hadoop MapReduce environment. In addition, the HDAOML‐BDE technique uses manta ray foraging optimization‐based feature selection (MRFO‐FS) technique to reduce high dimensionality problems. Moreover, the HDAOML‐BDE method uses relevance vector machine (RVM) model for the healthcare data environment. Furthermore, the arithmetic optimization algorithm (AOA) is utilized for the parameter tuning of the RVM classifier. The simulation results of the HDAOML‐BDE technique are tested on a healthcare dataset, and the outcomes portray the improved performance of the HDAOML‐BDE strategy over recent approaches in different measures.

Unveiling the Landscape of Big Data Analytics in Healthcare: A Comprehensive Bibliometric Analysis

In the rapidly evolving landscape of healthcare, the digital transformation marked by healthcare 4.0 has spurred a surge in data generation, giving rise to ‘big data’. Big data analytics has become an effective tool in the healthcare industry, revolutionising medical research, patient care, and healthcare management. This study undertakes a meticulous bibliometric analysis, drawing upon a dataset of 2212 articles from the Scopus database spanning 2014 to 2023, to unravel the trajectory of big data analytics in healthcare. The research explores diverse dimensions, from the distribution of studies across years to the productivity rankings of journals, countries, and institutions, elucidating the evolving trends and key contributors. Co-authorship networks and keyword co-occurrence analysis reveal thematic clusters and intellectual structures, contributing to a nuanced understanding of the field. The results underscore the escalating global interest in the fusion of big data and healthcare, illuminating collaborations, and identifying influential players. Additionally, the study identifies pressing challenges, including security concerns and skill shortages, emphasizing the imperative of overcoming these barriers for effective big data applications in healthcare. Serving as a valuable resource for researchers, practitioners, and policymakers, this research not only captures the current landscape but also provides insights for future exploration, contributing to strategic planning in this dynamic domain.

The role of big data in healthcare: A review of implications for patient outcomes and treatment personalization

The integration of big data analytics in healthcare has ushered in a transformative era, redefining the landscape of patient care and treatment strategies. This review examines the multifaceted implications of big data on patient outcomes and the individualization of medical interventions. Delving into the foundational elements of big data, we explore its evolution in the healthcare sector and highlight pivotal applications, such as predictive analytics, treatment personalization, and population health management. The paper underscores how big data-driven insights have revolutionized diagnosis and early detection, leading to more accurate and timely interventions. Treatment planning has witnessed a paradigm shift, with the tailoring of therapeutic approaches based on robust data analyses, fostering the realization of personalized medicine. Moreover, the role of big data in enhancing patient engagement and empowerment is explored, illuminating the potential for collaborative and informed decision-making. Despite these advancements, ethical considerations and challenges loom large. Privacy concerns, data security, and the ethical use of patient information demand meticulous attention to ensure the responsible application of big data in healthcare. The paper discusses the evolving regulatory frameworks and strategies to address these pressing issues. Looking ahead, the review outlines emerging trends and technologies poised to shape the future of big data in healthcare. It identifies research opportunities and encourages interdisciplinary collaborations to further propel innovation in this dynamic field. By addressing challenges and envisioning future possibilities, it seeks to contribute to the ongoing dialogue surrounding the responsible and impactful integration of big data in shaping the future of healthcare.

A Brief Review of Big Data in Healthcare: Challenges and Issues, Recent Developments, and Future Directions

Big data is improving the healthcare industry and creating opportunities for improved patient care, personalized medicine, and advanced research. This brief review article aims to survey the challenges and issues connected with big data in healthcare, discuss recent developments in the field, and highlight future directions for helping big data to enhance healthcare outcomes by understanding these aspects, participants can make informed decisions and contribute to the advancement of big data analytics in healthcare.

Exploring the Use of Big Data Analytics in Healthcare: Opportunities and Challenges

The rapid growth of digital technologies and the availability of large-scale healthcare data have opened up new possibilities for leveraging big data analytics in healthcare. This paper explores the opportunities and challenges associated with the use of big data analytics in the healthcare industry. It highlights the potential benefits of applying advanced analytics techniques to vast amounts of healthcare data, including improved disease prediction, personalized medicine, and enhanced patient care. The paper also discusses the challenges involved in implementing big data analytics in healthcare, such as data privacy and security concerns, data quality issues, and the need for skilled data scientists and IT infrastructure. Furthermore, it explores the ethical considerations and regulatory frameworks that need to be addressed when utilizing big data analytics in healthcare

Examining Factors Influencing the Acceptance of Big Data Analytics in Healthcare

Examining Factors Influencing the Acceptance of Big Data Analytics in Healthcare

Using Healthcare Big Data Analytics to Improve Women's Health: Benefits, Challenges, and Perspectives.

Using Healthcare Big Data Analytics to Improve Women's Health: Benefits, Challenges, and Perspectives.

A Comprehensive Study of Various Techniques for Big Data Analytics in Healthcare

'Enormous information' is gigantic measures of data that can do something amazing. Different hotspots for massive data in the medical care business include emergency clinical assessment outcomes, clinic data, patient clinical records and IoT gadgets. Biomedical examination also generates a significant amount of substantial data that is useful to public health services. As a result, several businesses, including the medical care industry, are figuring out how to transform this potential into improved services and financial profits. Modern medical service associations may upset clinical therapies (treatments) and personalized medication with a robust blend of bio-medical and medical services information. Huge Data examination can work on understanding results, advance and customize care, further develop supplier associations with patients, and lessen clinical spending. This paper discuss analytic techniques used in Big Data healthcare.

Data Visualization of Big Data for Predictive and Descriptive Analytics for Stroke, COVID-19, and Diabetes

Visualization of big data is crucial for meaningful interpretations and especially for healthcare. Brief discussions are made for big data, background for healthcare, and recent work in big data analytics for healthcare. This research pertains to different levels of big data: 5,110 vs. 101,766 vs. 320,200 vs. 1 million data values. Data visualizations and predictive analytics are presented of big data for selected diseases of stroke with 5,110 data values, diabetes with 101,766 data values, and two COVID-19 studies: one with 320,200 data values and another with 1 million data values. Data visualizations are generated for these diseases with big data using Tableau. For stroke patients, an investigation was performed to determine how different living environments affect relationship between strokes. The data visualizations for diabetes showed impact of insulin use yielded reduced hospital stays. Data visualizations for COVID-19 provided temporal trends in confirmed cases, mortality, and recovery rates for 2020-2023. Conclusions and future directions of research are presented.

Challenges and opportunities of big data analytics in healthcare

AbstractData science is an interdisciplinary discipline that employs big data, machine learning algorithms, data mining techniques, and scientific methodologies to extract insights and information from massive amounts of structured and unstructured data. The healthcare industry constantly creates large, important databases on patient demographics, treatment plans, results of medical exams, insurance coverage, and more. The data that IoT (Internet of Things) devices collect is of interest to data scientists. Data science can help with the healthcare industry's massive amounts of disparate, structured, and unstructured data by processing, managing, analyzing, and integrating it. To get reliable findings from this data, proper management and analysis are essential. This article provides a comprehensive study and discussion of process data analysis as it pertains to healthcare applications. The article discusses the advantages and disadvantages of using big data analytics (BDA) in the medical industry. The insights offered by BDA, which can also aid in making strategic decisions, can assist the healthcare system.

Assessing the Big Data Adoption Readiness Role in Healthcare between Technology Impact Factors and Intention to Adopt Big Data

Big data is quickly becoming a new area where administrative work can be improved. Even so, it is still in the early stages of being used in hospitals in countries with less technology. Therefore, there is an inadequate grasp of the evaluation of big data adoption preparedness in the healthcare sector as data-point-determined insights become crucially useful in healthcare institutions in underdeveloped nations. This process, called “digital transformation,” has a lot of benefits; for example, it helps healthcare organizations to create more efficient processes, offer different services, give better care, make more money, and cut costs. This paper aims to suggest and assess a conceptual framework that focuses on technological factors and can assist in determining the readiness of healthcare institutions in developing nations to utilize big data. Although the study can offer valuable perspectives on the advantages that can arise from adopting big data in the healthcare sector, it is important to highlight that leveraging big data analytics in healthcare has the potential to enhance the efficiency and effectiveness of healthcare services. This, in turn, can indirectly contribute to sustainability objectives by optimizing the allocation of resources, minimizing waste, and improving patient outcomes. A total of 328 healthcare workers from Malaysia were subjected to experimental testing of the model. The collected data were evaluated using the Smart PLS 3 program and the structural equation model (SEM). The study’s findings supported our hypotheses. The results showed that technological factors affected the participants’ perception of their readiness for big data, which ultimately influenced their interest in utilizing it. By concentrating on big data preparedness in the healthcare industry and ambition to utilize big data, this research provides an important theoretical contribution. Employees who are “big data ready” would benefit from the study’s results, as, through their recognition, said employees are more likely to increase the desire to use big data in Malaysia’s healthcare sectors.

A Review of the Role of Big Data Analytics in Healthcare

The healthcare industry generates massive amounts of data, from patient records to clinical trial results, which can be utilized to improve patient outcomes, reduce costs, and enhance the efficiency of healthcare delivery. Big data analytics is a technology that can help healthcare organizations make sense of these vast amounts of data. The potential benefits of big data analytics in healthcare include improved patient outcomes, reduced costs, enhanced efficiency, and more accurate diagnoses. However, big data analytics also presents significant challenges, including data privacy and security, and the complexity of healthcare data. This research paper aims to explore the role of big data analytics in healthcare, focusing on its potential benefits, challenges, and applications. By understanding the potential of big data analytics in healthcare and the challenges it presents, healthcare organizations can leverage this technology to improve healthcare delivery and patient outcomes.

Big Data Analytics in Healthcare: Exploring the Role of Machine Learning in Predicting Patient Outcomes and Improving Healthcare Delivery

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research

Big Data Analytics in Healthcare: An Analytical Survey

Big Data Analytics in Healthcare: An Analytical Survey

Big Data Analytics in Healthcare: Opportunities and Challenges

The use of big data analytics in healthcare is growing rapidly as a result of the increasing availability of large, complex, and diverse data sets. Big data analytics can be used to improve patient outcomes, reduce costs, and enhance clinical decision-making. However, there are also significant challenges associated with the use of big data analytics in healthcare. This paper provides an overview of the opportunities and challenges of big data analytics in healthcare, with a focus on the potential benefits of using big data analytics in healthcare, the challenges of implementing big data analytics in healthcare, and the ethical considerations that must be taken into account.

Real time health care big data analytics model for improved QoS in cardiac disease prediction with IoT devices

Real time health care big data analytics model for improved QoS in cardiac disease prediction with IoT devices

Healthcare analytics in non-profits: Evidence from North America

Background: The implementation of Big Data analytics in healthcare has an incredible chance of improving the quality of care, minimizing waste and error, and also decreasing the cost of care. Purpose: This systematic comment of literature objectives to discover the assortment of Big Data analytics in healthcare, including its applications and challenges in the adoption of its in healthcare. Furthermore, it intends to figure out the strategies to overcome the challenges. Data sources: An organized search of the articles was carried out on five primary scientific databases: ScienceDirect, Taylor, Francis and Emerald. The information articles on Big Data analytics in healthcare published from January 2017 to January 2022 are deemed. Data extraction: two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in the healthcare field; challenges and strategies to overcome the issues in healthcare. Results: A complete of fifty eight articles are selected as per the inclusion needs and examined. The analyses of these posts placed that scientists do not have consensus about the useful definition of Big Data in serious healthcare. Big Data analytics finds the application for healthcare option support, optimization of healthcare operations, and reduction of treatment cost. The chief fight in serious adoption of Big Data analytics is non accessibility of evidence of its in healthcare. Conclusion: This review analysis unveils that there is a paucity of information on evidence of world utilization that is real of Big Data analytics in healthcare. Keywords: Big data, healthcare analytics.

HOW HEALTHCARE INDUSTRY CAN LEVERAGE BIG DATA ANALYTICS TECHNOLOGY AND TOOLS FOR EFFICIENT MANAGEMENT

Recently, huge quantities of organised, unstructured, and semi structured data are being produced by different systems and machines around the world. Such a huge volume of heterogenous data set that typically cannot be managed by traditional system is described as big data. The healthcare sector continues to be confronted by the importance of managing the fundamental information created by numerous sources, known for creating high volumes of heterogeneous information. Different big data analytics programs, as well as strategies, have already been created for managing these substantial quantities of information in the healthcare market. In this particular paper, we go over the effect of big data on healthcare, as well as different methods offered in the Hadoop ecosystem for managing it. We also examine the conceptual structure of big data analytics for healthcare, which involves the information collecting historical past of various limbs, the genome database, electronic health records, text/imagery, and then the medical conclusions support program.