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dc.contributor.authorMcKenzie, Kirsten
dc.contributor.authorCampbell, Margaret
dc.contributor.authorScott, Deborah
dc.contributor.authorDiscoll, Tim
dc.contributor.authorHarrison, James
dc.contributor.authorMcclure, Roderick
dc.date.accessioned2017-05-03T14:19:01Z
dc.date.available2017-05-03T14:19:01Z
dc.date.issued2010
dc.date.modified2011-03-08T06:49:27Z
dc.identifier.issn14726947
dc.identifier.doi10.1186/1472-6947-10-19
dc.identifier.urihttp://hdl.handle.net/10072/36041
dc.description.abstractBackground Work-related injuries in Australia are estimated to cost around $57.5 billion annually, however there are currently insufficient surveillance data available to support an evidence-based public health response. Emergency departments (ED) in Australia are a potential source of information on work-related injuries though most ED's do not have an 'Activity Code' to identify work-related cases with information about the presenting problem recorded in a short free text field. This study compared methods for interrogating text fields for identifying work-related injuries presenting at emergency departments to inform approaches to surveillance of work-related injury. Methods Three approaches were used to interrogate an injury description text field to classify cases as work-related: keyword search, index search, and content analytic text mining. Sensitivity and specificity were examined by comparing cases flagged by each approach to cases coded with an Activity code during triage. Methods to improve the sensitivity and/or specificity of each approach were explored by adjusting the classification techniques within each broad approach. Results The basic keyword search detected 58% of cases (Specificity 0.99), an index search detected 62% of cases (Specificity 0.87), and the content analytic text mining (using adjusted probabilities) approach detected 77% of cases (Specificity 0.95). Conclusions The findings of this study provide strong support for continued development of text searching methods to obtain information from routine emergency department data, to improve the capacity for comprehensive injury surveillance.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent467897 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherBioMed Central Ltd
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto10
dc.relation.ispartofjournalBMC Medical Informatics and Decision Making
dc.relation.ispartofvolume10
dc.rights.retentionY
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchcode4609
dc.subject.fieldofresearchcode3202
dc.titleIdentifying work related injuries: comparison of methods for interrogating text fields
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/2.0
gro.rights.copyright© 2010 McKenzie et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.date.issued2010
gro.hasfulltextFull Text
gro.griffith.authorMcClure, Roderick J.


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