Big data analytics in healthcare: promise and potential

Objective To describe the promise and potential of big data analytics in healthcare. Methods The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome

Big data analytics in the healthcare industry: A systematic review and roadmap for practical implementation in Nigeria

Introduction: The introduction of digitization of healthcare data has posed both challenges and opportunities within the industry. Big Data Analytics (BDA) has emerged as a powerful tool, facilitating data-driven decision-making and revolutionizing patient care. Purpose: The research aimed to analyze diverse perspectives on big data in healthcare, assess BDA's application in the sector, examine contexts, synthesize findings, and propose an implementation roadmap and future research directions. Methodology: Using an SLR protocol by Nazir et al. (2019), sources like Google Scholar, IEEE, ScienceDirect, Springer, and Elsevier were searched with 18 queries. Inclusion criteria yielded 37 articles, with five more added through citation searches, totaling 42. Results: The study uncovers diverse healthcare viewpoints on big data's transformative potential, precision medicine, resource optimization, and challenges like security and interoperability. BDA empowers clinical choices, early disease detection, and personalized medicine. Future areas include ethics, interpretable AI, real-time BDA, multi-omics integration, AI-driven drug discovery, mental health, resource constraints, health disparities, secure data sharing, and human-AI collaboration. Conclusion: This study illuminates Big Data Analytics' transformative potential in healthcare, revealing diverse applications and emphasizing ethical complexities. Integrated data analysis is advocated for patient-centric services. Recommendation: Balancing BDA's power with privacy, guidelines, and regulations is vital. Implementing the Nigerian healthcare roadmap can optimize outcomes, address challenges, and enhance efficiency. Future research should focus on ethics, interpretable AI, real-time BDA, and mental health integration

No VIP Treatment: ACOs Should Not Get Waiver Protection from the Prohibition on Beneficiary Inducement

Virgil is known for saying the greatest wealth is health. \u27 Based on the astronomical amount spent on healthcare, the United States has taken his idea literally-spending more wealth will lead to greater health. In 2006, the United States spent over seven thousand dollars per person annually on healthcare. While that number may not seem very high to spend on an individual level, the total amounted to approximately 2.1 trillion dollars in 2006. In 2014, that number hit three trillion, or seventeen percent of the country\u27s Gross Domestic Product ( GDP ). One justification for spending nearly one-fifth of the United States GDP on health care is that high quality health outcomes will result. However, this causal leap depends on the assumption that spending more money on healthcare automatically leads to high quality, which is simply not the case

Technology and Caregiving: Emerging Interventions and Directions for Research.

An array of technology-based interventions has increasingly become available to support family caregivers, primarily focusing on health and well-being, social isolation, financial, and psychological support. More recently the emergence of new technologies such as mobile and cloud, robotics, connected sensors, virtual/augmented/mixed reality, voice, and the evermore ubiquitous tools supported by advanced data analytics, coupled with the integration of multiple technologies through platform solutions, have opened a new era of technology-enabled interventions that can empower and support family caregivers. This paper proposes a conceptual framework for identifying and addressing the challenges that may need to be overcome to effectively apply technology-enabled solutions for family caregivers. The paper identifies a number of challenges that either moderate or mediate the full use of technologies for the benefit of caregivers. The challenges include issues related to equity, inclusion, and access; ethical concerns related to privacy and security; political and regulatory factors affecting interoperability and lack of standards; inclusive/human-centric design and issues; and inherent economic and distribution channel difficulties. The paper concludes with a summary of research questions and issues that form a framework for global research priorities

From Data to Decision: An Implementation Model for the Use of Evidence-based Medicine, Data Analytics, and Education in Transfusion Medicine Practice

Healthcare in the United States is underperforming despite record increases in spending. The causes are as myriad and complex as the suggested solutions. It is increasingly important to carefully assess the appropriateness and cost-effectiveness of treatments especially the most resource-consuming clinical interventions. Healthcare reimbursement models are evolving from fee-for-service to outcome-based payment. The Patient Protection and Affordable Care Act has added new incentives to address some of the cost, quality, and access issues related to healthcare, making the use of healthcare data and evidence-based decision-making essential strategies. However, despite the great promise of these strategies, the transition to data-driven, evidence-based medical practice is complex and faces many challenges. This study aims to bridge the gaps that exist between data, knowledge, and practice in a healthcare setting through the use of a comprehensive framework to address the administrative, cultural, clinical, and technical issues that make the implementation and sustainability of an evidence-based program and utilization of healthcare data so challenging. The study focuses on promoting evidence-based medical practice by leveraging a performance management system, targeted education, and data analytics to improve outcomes and control costs. The framework was implemented and validated in transfusion medicine practice. Transfusion is one of the top ten coded hospital procedures in the United States. Unfortunately, the costs of transfusion are underestimated and the benefits to patients are overestimated. The particular aim of this study was to reduce practice inconsistencies in red blood cell transfusion among hospitalists in a large urban hospital using evidence-based guidelines, a performance management system, recurrent reporting of practice-specific information, focused education, and data analytics in a continuous feedback mechanism to drive appropriate decision-making prior to the decision to transfuse and prior to issuing the blood component. The research in this dissertation provides the foundation for implementation of an integrated framework that proved to be effective in encouraging evidence-based best practices among hospitalists to improve quality and lower costs of care. What follows is a discussion of the essential components of the framework, the results that were achieved and observations relative to next steps a learning healthcare organization would consider

No equity, no triple aim: strategic proposals to advance health equity in a volatile policy environment

Health professionals, including social workers, community health workers, public health workers, and licensed health care providers, share common interests and responsibilities in promoting health equity and improving social determinants of health—the conditions in which we live, work, play, and learn. This article summarizes underlying causes of health inequity and comparatively poor health outcomes in the U.S. It describes barriers to realizing the hope embedded in the 2010 Patient Protection and Affordable Care Act that moving away from fee-for-service payments will naturally drive care upstream as providers respond to greater financial risk for the health of their patients by undertaking greater prevention efforts. The article asserts that health equity should serve as the guiding framework for achieving the Triple Aim of health care reform. It outlines practical opportunities for improving care and for promoting stronger efforts to address social determinants of health. These proposals include developing a dashboard of measures to assist providers committed to health equity and community-based prevention and to promote institutional accountability for addressing socio-economic factors that influence health

Investigating the dimensions, components, and key indicators of the use of big data in the health industry

oai:ojs2.journal-data.ir:article/1Using big data analytics in healthcare has positive as well as life-saving results. Big data refers to the vast amounts of information generated by the digitization of everything that is synthesized and analyzed by specific technologies. Here Big Data uses health services to use specific health data of a population (or a specific individual) and potentially help prevent disease pandemics, treat diseases, reduce costs, and more. In the field of health, big data covers a wide range of information, including physiological, behavioral, molecular, clinical, medical imaging, disease management, medication history, nutrition, or exercise parameters. Big Data Analysis In the field of health, it is a complex process of examining big data to discover information. This information includes hidden patterns, market trends, unknown correlations, and customer preferences. Information analysis can help organizations make informed business and clinical decisions. The medical data-driven industry is the most complex among industries. Not only is this data available from a variety of sources, but it must also comply with government regulations. This process is difficult and delicate and requires some level of security and communication. Due to the importance of this issue, in this article, after introducing the types of data available in the health industry, the characteristics and sources of big data in health are defined and an analytical model for the use of large data in the health industry is presented. This model helps to understand the dimensions, components, and key elements of using big data in the health industry