Journal ArticleDOI
Managing Data in Healthcare Information Systems: Many Models, One Solution
Karamjit Kaur,Rinkle Rani +1 more
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A polyglot-persistent framework combines relational, graph, and document data models to accommodate information variety in healthcare data.Abstract:
Because healthcare data comes from multiple, vastly different sources, databases must adopt a range of models to process and store it. A polyglot-persistent framework combines relational, graph, and document data models to accommodate information variety.read more
Citations
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Big Data Analytics in Healthcare
Ashwin Belle,Raghuram Thiagarajan,S. M. Reza Soroushmehr,Fatemeh Navidi,Daniel A. Beard,Kayvan Najarian +5 more
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.
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Analyzing the performance of a blockchain-based personal health record implementation
Alex Roehrs,Cristiano André da Costa,Rodrigo da Rosa Righi,Valter Ferreira da Silva,José Roberto Goldim,Douglas C. Schmidt +5 more
TL;DR: The performance results indicated that data distributed via a blockchain could be recovered with low average response time and high availability in the scenarios the authors tested and demonstrated how OmniPHR model implementation can integrate distributed data into a unified view of health records.
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Big data for secure healthcare system: a conceptual design
TL;DR: This article builds a distributed framework of organized healthcare model for the purpose of protecting patient data in a distributed system.
Journal ArticleDOI
Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities
TL;DR: The emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare i.e.medical image analysis and imaging informatics, bioinformatics, clinical informatic, public health informatics and medical signal analytics is presented.
Journal ArticleDOI
Big data analytics in healthcare − A systematic literature review and roadmap for practical implementation
TL;DR: A comprehensive roadmap to derive insights from BDA in the healthcare ( patient care ) domain is presented, based on the results of a systematic literature review, and a state-of-the-art BDA architecture called Med-BDA for healthcare domain is proposed which solves all current BDA challenges and isbased on the latest zeta big data paradigm.
References
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Book
Big data: The next frontier for innovation, competition, and productivity
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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From the authors
P. Palange,S. A. Ward +1 more
TL;DR: The members of the European Respiratory Society Task Force on Exercise Testing in Clinical Practice have read with interest the letter from J.E. Cotes and J.W. Reed and are of the opinion that any response to the points raised therein should be placed in the context of a recently published Task Force 1.
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What you always wanted to know about Datalog (and never dared to ask)
TL;DR: The syntax and semantics of Datalog and its use for querying a relational database are presented, and the most relevant methods for achieving efficient evaluations of Daloog queries are presented.
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Query reformulation for dynamic information integration
TL;DR: This paper describes the query reformulation process in SIMS and the operators used in it, and provides precise definitions of the reformulation operators and the rationale behind choosing the specific ones SIMS uses.
Journal ArticleDOI
The Emergence of National Electronic Health Record Architectures in the United States and Australia: Models, Costs, and Questions
Tracy D. Gunter,Nicolas P. Terry +1 more
TL;DR: The article describes two national electronic health record models (currently developing in the United States and Australia) and one distributed, personal model and contrasts the US and Australian models in their different architectures and approaches to patient autonomy, privacy, and confidentiality.