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Journal ArticleDOI

Big data analytics in healthcare: promise and potential

07 Feb 2014-Vol. 2, Iss: 1, pp 3-3

TL;DR: 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, and its potential is great; however there remain challenges to overcome.
Abstract: 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.
Topics: Analytics (72%), Big data (61%)
Citations
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Journal ArticleDOI
TL;DR: This paper presents a comprehensive discussion on state-of-the-art big data technologies based on batch and stream data processing based on structuralism and functionalism paradigms and strengths and weaknesses of these technologies are analyzed.
Abstract: We use structuralism and functionalism paradigms to analyze the origins of big data applications.Current trends and sources of big data.Processing technologies, methods and analysis techniques for big data are compared in detail.We analyze major challenges with big data and also discussed several opportunities.Case studies and emerging technologies for big data problems are discussed. Big data is a potential research area receiving considerable attention from academia and IT communities. In the digital world, the amounts of data generated and stored have expanded within a short period of time. Consequently, this fast growing rate of data has created many challenges. In this paper, we use structuralism and functionalism paradigms to analyze the origins of big data applications and its current trends. This paper presents a comprehensive discussion on state-of-the-art big data technologies based on batch and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also discusses big data analytics techniques, processing methods, some reported case studies from different vendors, several open research challenges, and the opportunities brought about by big data. The similarities and differences of these techniques and technologies based on important parameters are also investigated. Emerging technologies are recommended as a solution for big data problems.

877 citations


Journal ArticleDOI
TL;DR: The historical development, architectural design and component functionalities of big data analytics, including analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability and traceability are examined.
Abstract: To date, health care industry has not fully grasped the potential benefits to be gained from big data analytics. While the constantly growing body of academic research on big data analytics is mostly technology oriented, a better understanding of the strategic implications of big data is urgently needed. To address this lack, this study examines the historical development, architectural design and component functionalities of big data analytics. From content analysis of 26 big data implementation cases in healthcare, we were able to identify five big data analytics capabilities: analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability, and traceability. We also mapped the benefits driven by big data analytics in terms of information technology (IT) infrastructure, operational, organizational, managerial and strategic areas. In addition, we recommend five strategies for healthcare organizations that are considering to adopt big data analytics technologies. Our findings will help healthcare organizations understand the big data analytics capabilities and potential benefits and support them seeking to formulate more effective data-driven analytics strategies.

654 citations


Cites background from "Big data analytics in healthcare: p..."

  • ...…from business intelligence and decision support systems enable healthcare organizations to analyze an immense volume, variety and velocity of data across a wide range of healthcare networks to support evidence-based decision making and action taking (Watson, 2014; Raghupathi & Raghupathi, 2014)....

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Journal ArticleDOI
Qi Xia1, Emmanuel Boateng Sifah1, Kwame Omono Asamoah1, Jianbin Gao1  +2 moreInstitutions (3)
24 Jul 2017-IEEE Access
TL;DR: The proposed MeDShare system is blockchain-based and provides data provenance, auditing, and control for shared medical data in cloud repositories among big data entities and employs smart contracts and an access control mechanism to effectively track the behavior of the data.
Abstract: The dissemination of patients’ medical records results in diverse risks to patients’ privacy as malicious activities on these records cause severe damage to the reputation, finances, and so on of all parties related directly or indirectly to the data. Current methods to effectively manage and protect medical records have been proved to be insufficient. In this paper, we propose MeDShare, a system that addresses the issue of medical data sharing among medical big data custodians in a trust-less environment. The system is blockchain-based and provides data provenance, auditing, and control for shared medical data in cloud repositories among big data entities. MeDShare monitors entities that access data for malicious use from a data custodian system. In MeDShare, data transitions and sharing from one entity to the other, along with all actions performed on the MeDShare system, are recorded in a tamper-proof manner. The design employs smart contracts and an access control mechanism to effectively track the behavior of the data and revoke access to offending entities on detection of violation of permissions on data. The performance of MeDShare is comparable to current cutting edge solutions to data sharing among cloud service providers. By implementing MeDShare, cloud service providers and other data guardians will be able to achieve data provenance and auditing while sharing medical data with entities such as research and medical institutions with minimal risk to data privacy.

521 citations


Journal ArticleDOI
Bahar Farahani1, Farshad Firouzi2, Victor Chang3, Mustafa Badaroglu4  +2 moreInstitutions (5)
TL;DR: It is proposed that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other, and needs a multi-layer architecture.
Abstract: Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device–network–human interfaces, security, and privacy

491 citations


Cites background from "Big data analytics in healthcare: p..."

  • ...In other worlds, IoT eHealth has to handle the complexity of the data in terms of their variety, volume and velocity [123], [124], [125]....

    [...]


Journal ArticleDOI
TL;DR: Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed and potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are examined.
Abstract: The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

382 citations


Cites background from "Big data analytics in healthcare: p..."

  • ...Research community has interest in consuming data captured from live monitors for developing continuousmonitoring technologies [94, 95]....

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TL;DR: The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.
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4,551 citations


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TL;DR: This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform.
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1,265 citations


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Sachchidanand Singh1, Nirmala Singh2Institutions (2)
31 Dec 2012-
TL;DR: This analysis illustrates that the Big Data analytics is a fast-growing, influential practice and a key enabler for the social business and is critical for success in the age of social media.
Abstract: In this paper, we explain the concept, characteristics & need of Big Data & different offerings available in the market to explore unstructured large data. This paper covers Big Data adoption trends, entry & exit criteria for the vendor and product selection, best practices, customer success story, benefits of Big Data analytics, summary and conclusion. Our analysis illustrates that the Big Data analytics is a fast-growing, influential practice and a key enabler for the social business. The insights gained from the user generated online contents and collaboration with customers is critical for success in the age of social media.

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Proceedings ArticleDOI
Jian-Guo Bian1, Umit Topaloglu1, Fan Yu1Institutions (1)
29 Oct 2012-
TL;DR: An approach to find drug users and potential adverse events by analyzing the content of twitter messages utilizing Natural Language Processing (NLP) and to build Support Vector Machine (SVM) classifiers is described, suggesting that daily-life social networking data could help early detection of important patient safety issues.
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213 citations


Book
01 Mar 2021-
Abstract: Unique insights to implement big data analytics and reap big returns to your bottom lineFocusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantageTakes an in-depth look at the financial value of big data analyticsOffers tools and best practices for working with big dataOnce the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.

184 citations


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Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
202212
2021262
2020337
2019354
2018349
2017269