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Big Data in the Era of Health Information Exchanges: Challenges and Opportunities for Public Health

Published on Nov 10, 2017
· DOI :10.3390/informatics4040039
Janet G. Baseman11
Estimated H-index: 11
,
Debra Revere11
Estimated H-index: 11
,
Ian Painter10
Estimated H-index: 10
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Abstract
Public health surveillance of communicable diseases depends on timely, complete, accurate, and useful data that are collected across a number of healthcare and public health systems. Health Information Exchanges (HIEs) which support electronic sharing of data and information between health care organizations are recognized as a source of ‘big data’ in healthcare and have the potential to provide public health with a single stream of data collated across disparate systems and sources. However, given these data are not collected specifically to meet public health objectives, it is unknown whether a public health agency’s (PHA’s) secondary use of the data is supportive of or presents additional barriers to meeting disease reporting and surveillance needs. To explore this issue, we conducted an assessment of big data that is available to a PHA—laboratory test results and clinician-generated notifiable condition report data—through its participation in a HIE.
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References12
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Published on Nov 21, 2016in JMIR medical informatics
Clemens Scott Kruse12
Estimated H-index: 12
,
Rishi Goswamy1
Estimated H-index: 1
+ 1 AuthorsSarah Marawi1
Estimated H-index: 1
Background: Big data analytics offers promise in many business sectors, and health care is looking at big data to provide answers to many age-related issues, particularly dementia and chronic disease management. Objective: The purpose of this review was to summarize the challenges faced by big data analytics and the opportunities that big data opens in health care. Methods: A total of 3 searches were performed for publications between January 1, 2010 and January 1, 2016 (PubMed/MEDLINE, CINAHL, ...
Published on Jan 1, 2016in Journal of Public Health Management and Practice 1.42
Gulzar H. Shah12
Estimated H-index: 12
,
Jonathon P. Leider10
Estimated H-index: 10
+ 1 AuthorsRavneet Kaur1
Estimated H-index: 1
Local health departments (LHDs) are presented with an unprecedented opportunity to use real-time, standardized data to inform public health practice in a post–Affordable Care Act era marked by interorganizational collaborations and availability of large amounts of electronic health care data through health information exchanges.1–4 In a dynamic public health environment filled with emerging demands for evidence-based public health practice, it is ever more imperative for LHDs to harness these da...
Published on Dec 1, 2015in EPJ Data Science 3.26
Benjamin M. Althouse22
Estimated H-index: 22
(SFI: Santa Fe Institute),
Samuel V. Scarpino16
Estimated H-index: 16
(SFI: Santa Fe Institute)
+ 33 AuthorsDerek A. T. Cummings42
Estimated H-index: 42
(Johns Hopkins University)
Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement...
Published on Jun 1, 2015in Big Data Research
Xiaolong Jin15
Estimated H-index: 15
(CAS: Chinese Academy of Sciences),
Benjamin W. Wah32
Estimated H-index: 32
(CUHK: The Chinese University of Hong Kong)
+ 1 AuthorsYuanzhuo Wang10
Estimated H-index: 10
(CAS: Chinese Academy of Sciences)
In recent years, the rapid development of Internet, Internet of Things, and Cloud Computing have led to the explosive growth of data in almost every industry and business area. Big data has rapidly developed into a hot topic that attracts extensive attention from academia, industry, and governments around the world. In this position paper, we first briefly introduce the concept of big data, including its definition, features, and value. We then identify from different perspectives the significan...
Published on Oct 1, 2014in Royal Society Open Science 2.52
Tobias Preis25
Estimated H-index: 25
(Warw.: University of Warwick),
Helen Susannah Moat13
Estimated H-index: 13
(Warw.: University of Warwick)
Seasonal influenza outbreaks and pandemics of new strains of the influenza virus affect humans around the globe. However, traditional systems for measuring the spread of flu infections deliver results with one or two weeks delay. Recent research suggests that data on queries made to the search engine Google can be used to address this problem, providing real-time estimates of levels of influenza-like illness in a population. Others have however argued that equally good estimates of current flu l...
Published on Sep 17, 2014in The American Journal of Managed Care 1.71
Seth Joseph3
Estimated H-index: 3
,
Max Sow3
Estimated H-index: 3
+ 2 AuthorsMa and Mary Ann Chaffee1
Estimated H-index: 1
Published on Aug 11, 2014in PLOS ONE 2.78
Michael A. Johansson23
Estimated H-index: 23
,
Ann M. Powers38
Estimated H-index: 38
+ 2 AuthorsJ. Erin Staples30
Estimated H-index: 30
Background In December 2013, the first locally-acquired chikungunya virus (CHIKV) infections in the Americas were reported in the Caribbean. As of May 16, 55,992 cases had been reported and the outbreak was still spreading. Identification of newly affected locations is paramount to intervention activities, but challenging due to limitations of current data on the outbreak and on CHIKV transmission. We developed models to make probabilistic predictions of spread based on current data considering ...
Published on Jul 1, 2014in Health Affairs 5.71
Joachim Roski1
Estimated H-index: 1
(Booz Allen Hamilton),
George W. Bo-Linn1
Estimated H-index: 1
,
Timothy A. Andrews1
Estimated H-index: 1
(Booz Allen Hamilton)
Big data has the potential to create significant value in health care by improving outcomes while lowering costs. Big data’s defining features include the ability to handle massive data volume and variety at high velocity. New, flexible, and easily expandable information technology (IT) infrastructure, including so-called data lakes and cloud data storage and management solutions, make big-data analytics possible. However, most health IT systems still rely on data warehouse structures. Without t...
Published on Jul 1, 2014in North Carolina medical journal
Anne Marie Meyer19
Estimated H-index: 19
(UNC: University of North Carolina at Chapel Hill),
Andrew F. Olshan60
Estimated H-index: 60
(UNC: University of North Carolina at Chapel Hill)
+ 4 AuthorsWilliam R. Carpenter24
Estimated H-index: 24
(UNC: University of North Carolina at Chapel Hill)
The Integrated Cancer Information and Surveillance System (ICISS) facilitates population-based cancer research by developing extensive information technology systems that can link and manage large data sets. Taking an interdisciplinary “team science” approach, ICISS has developed data, systems, and methods that allow researchers to better leverage the power of big data to improve population health.
Published on Sep 1, 2013
Meredith Barrett4
Estimated H-index: 4
,
Olivier Humblet13
Estimated H-index: 13
+ 1 AuthorsNancy E. Adler74
Estimated H-index: 74
Abstract Big data is often discussed in the context of improving medical care, but it also has a less appreciated but equally important role to play in preventing disease. Big data can facilitate action on the modifiable risk factors that contribute to a large fraction of the chronic disease burden, such as physical activity, diet, tobacco use, and exposure to pollution. It can do so by facilitating the discovery of risk factors for disease at population, subpopulation, and individual levels, an...
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