. Big data analytics is rapidly emerging as a transformative force in the healthcare sector, promising to improve patient outcomes and simultaneously reduce costs. The digitisation of healthcare data has given rise to an unprecedented volume and variety of information, encompassing clinical records, patient details, machine-generated data, and even insights from social media. This wealth of data holds immense potential for various medical applications, such as clinical decision support, disease surveillance, and population health management. However, harnessing the power of big data in healthcare is no small feat, given the challenges posed by its sheer volume, diversity, and complexity.The future of healthcare undoubtedly lies in the hands of big data analytics, and embracing this technological shift is paramount for the continued advancement of the industry
Top1. Introduction
The healthcare sector produces a substantial volume of data encompassing patient information, medication details, diseases, treatments, research findings, and more (Erer, 2012). A growing trend involves the digitization of this data for patients to ensure the utilization of healthcare analytics with a focus on diligent care., focusing on maintaining records, adhering to regulations, and fulfilling regulatory obligations are crucial aspects (Krumholz, 2014). The vast repository of healthcare big data encompasses medical data derived from the automated Entry and systems supporting medical decision-making., as well as reports compiled by the physicians, the prescriptions given by experts, the blood test reports, the several scans imaging, the medicine data, the health-insurance records, and other relevant information. This also comprises digital patient records (EPRs) and information produced by automated devices about vital sign monitoring sensors, and data from social media platforms like Twitter, blogs, websites, and Facebook plays a vital role in online communication.
The storage of the vast possibilities of big data within the medical sector encompasses the significant potential for transformative impact. enhance healthcare quality while simultaneously reducing costs. It supports various medical and healthcare functions, including clinical decision support, disease surveillance, as well as population health management. In 2011, health data in the USA alone exceeded 150 exabytes, with the capacity to expand further into zettabytes and yottabytes (Alliance, 2015). However, the massive scale of big data analysis poses a bottleneck, prompting healthcare experts to turn to computer sciences for addressing the conversion of data into actionable information and knowledge involves leveraging cutting-edge technologies within the field of data science. Key advancements include the utilization of Hadoop, employing unsupervised learning for the detection of concealed patterns, integrating graph analytics, and harnessing natural language processing. These emerging technologies are pivotal in facilitating the transformation of raw data into valuable insights and actionable intelligence. in extracting knowledge from documents and understanding human textual language. The overwhelming heterogeneity of medical related big data challenges healthcare practitioners' intuitive skills, emphasizing the essential for algorithms to establish correlations among various factors. The idea of a learning healthcare system (LHS) envisions medicine as an information science, where the cycle of processes is defined to acknowledge any healthcare delivery system's by-products at institutional, national, or international levels (Cunha, 2015). The structural framework of big data medical analytics resembles traditional healthcare informatics architecture but diverges in terms of processing. When dealing with big data, processing is distributed across multiple nodes, necessitating a shift from standalone systems to distributed processing (Basuthkar, 2016). Healthcare networks must be redefined as data sources that extend beyond internal to external, located in multiple places. This effort is exemplified by the IoT healthcare network (IoThNet) (Kaur, 2006). Data from social media, machine-to-machine interactions, big transaction records, biometric data, and human-generated data present a comprehensive set of information for healthcare analytics. This paper delves into examining diverse healthcare platforms and frameworks integrated with analytical algorithms and techniques. for preventing, predicting, diagnosing, and treating chronic diseases. The paper highlights key frameworks and healthcare systems, emphasizing their contributions, achievements, and challenges through a detailed taxonomy in Table 1. Unlike previous surveys that focused solely on algorithms or frameworks, this paper thoroughly explores both aspects of healthcare technology, aiming to determine the focal point of alignment for the base of Smart Health (Andreu-Perez, 2015) to fortify the LHS vision. Additionally, the paper underscores the importance of adhering to ideal healthcare conventions & the global bodies, for instance, regulations such as the health-Insurance Portability a& Accountability Act (HIPAA) (Rayner, 2001) and the guidelines established by the World Health Organization (WHO) are noteworthy examples (Srinivas, 2010).