CyberinfraV1, Pmc, Corpus, bibRecord, 000417

Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks

Identifieur interne : 000417 ( Pmc/Corpus ); précédent : 000416; suivant : 000418

Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks

Auteurs : H. Liyanage ; S. De Lusignan ; S-T. Liaw ; C. Kuziemsky ; F. Mold ; P. Krause ; D. Fleming ; S. Jones

Source :

Yearbook of Medical Informatics [ 0943-4747 ] ; 2014.

RBID : PMC:4287086

Abstract

SummaryBackground

Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive.

Objective

To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines. Method: We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars.

Results

We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowd-sourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the “internet of things”, and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources.

Conclusions

Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287086

DOI: 10.15265/IY-2014-0016
PubMed: 25123718
PubMed Central: 4287086

Links to Exploration step

PMC:4287086

Le document en format XML

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<author><name sortKey="Liyanage, H" sort="Liyanage, H" uniqKey="Liyanage H" first="H." last="Liyanage">H. Liyanage</name>
<affiliation><nlm:aff id="aff001"> Clinical Informatics & Health Outcomes research group, Department of Health Care Policy and Management,<institution>University of Surrey</institution>
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<affiliation><nlm:aff id="aff001"> Clinical Informatics & Health Outcomes research group, Department of Health Care Policy and Management,<institution>University of Surrey</institution>
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<author><name sortKey="Mold, F" sort="Mold, F" uniqKey="Mold F" first="F." last="Mold">F. Mold</name>
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<front><div type="abstract" xml:lang="en"><title>Summary</title>
<sec><title>Background</title>
<p>Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive.</p>
</sec>
<sec><title>Objective</title>
<p>To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines. Method: We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars.</p>
</sec>
<sec><title>Results</title>
<p>We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowd-sourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the “internet of things”, and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources.</p>
</sec>
<sec><title>Conclusions</title>
<p>Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.</p>
</sec>
</div>
</front>
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<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
  <front><journal-meta><journal-id journal-id-type="nlm-ta">Yearb Med Inform</journal-id>
<journal-id journal-id-type="publisher-id">YMI</journal-id>
<journal-title-group><journal-title>Yearbook of Medical Informatics</journal-title>
</journal-title-group>
<issn pub-type="ppub">0943-4747</issn>
<issn pub-type="epub">2364-0502</issn>
<publisher><publisher-name>Schattauer GmbH</publisher-name>
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<article-meta><article-id pub-id-type="pmid">25123718</article-id>
<article-id pub-id-type="pmc">4287086</article-id>
<article-id pub-id-type="doi">10.15265/IY-2014-0016</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Working Group Contributions</subject>
</subj-group>
</article-categories>
<title-group><article-title>Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks</article-title>
<subtitle>Contribution of the IMIA Primary Healthcare Working Group</subtitle>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Liyanage</surname>
<given-names>H.</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>de Lusignan</surname>
<given-names>S.</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Liaw</surname>
<given-names>S-T.</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kuziemsky</surname>
<given-names>C.</given-names>
</name>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
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<contrib contrib-type="author"><name><surname>Mold</surname>
<given-names>F.</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Krause</surname>
<given-names>P.</given-names>
</name>
<xref ref-type="aff" rid="aff004"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Fleming</surname>
<given-names>D.</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Jones</surname>
<given-names>S.</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label>
 Clinical Informatics & Health Outcomes research group, Department of Health Care Policy and Management,<institution>University of Surrey</institution>
,<addr-line>Guildford, Surrey, UK</addr-line>
</aff>
<aff id="aff002"><label>2</label>
 School of Public Health & Community Medicine,<institution>UNSW Medicine Australia</institution>
,<addr-line>NSW, Australia</addr-line>
</aff>
<aff id="aff003"><label>3</label>
 Telfer School of Management,<institution>University of Ottawa</institution>
,<addr-line>Ottawa, Ontario, Canada</addr-line>
</aff>
<aff id="aff004"><label>4</label>
 Department of Computing,<institution>University of Surrey</institution>
,<addr-line>Guildford, Surrey, UK</addr-line>
</aff>
<author-notes><corresp>Correspondence to:</corresp>
<corresp id="cor1">Simon de Lusignan, Clinical Informatics & Health Outcomes research group, Department of Health Care Policy and Management, University of Surrey, GUILDFORD, Surrey GU2 7XH, UK, <email>s.lusignan@surrey.ac.uk</email>
</corresp>
</author-notes>
<pub-date pub-type="epub"><day>15</day>
<month>8</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="collection"><year>2014</year>
</pub-date>
<volume>9</volume>
<issue>1</issue>
<fpage>27</fpage>
<lpage>35</lpage>
<permissions><copyright-statement>© IMIA and Schattauer GmbH 2014</copyright-statement>
<copyright-year>2014</copyright-year>
</permissions>
<abstract><title>Summary</title>
<sec><title>Background</title>
<p>Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive.</p>
</sec>
<sec><title>Objective</title>
<p>To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines. Method: We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars.</p>
</sec>
<sec><title>Results</title>
<p>We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowd-sourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the “internet of things”, and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources.</p>
</sec>
<sec><title>Conclusions</title>
<p>Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.</p>
</sec>
</abstract>
<kwd-group><title>Keywords</title>
<kwd>Population surveillance</kwd>
<kwd>medical record systems computerized</kwd>
<kwd>information science</kwd>
<kwd>immunization</kwd>
<kwd>public health</kwd>
</kwd-group>
<counts><fig-count count="6"></fig-count>
<table-count count="2"></table-count>
<equation-count count="0"></equation-count>
<ref-count count="88"></ref-count>
<page-count count="9"></page-count>
</counts>
</article-meta>
</front>
</pmc>
</record>

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Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks

Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks

Source :

Abstract

Links to Exploration step

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Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki