Error Taxonomy
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Health Search databasePMCAll DatabasesAssemblyBioProjectBioSampleBioSystemsBooksClinVarCloneConserved DomainsdbGaPdbVarESTGeneGenomeGEO DataSetsGEO ProfilesGSSGTRHomoloGeneMedGenMeSHNCBI Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedPubMed HealthSNPSparcleSRAStructureTaxonomyToolKitToolKitAllToolKitBookToolKitBookghUniGeneSearch taxonomy of error analysis termSearch Advanced Journal list Help Journal ListProc AMIA Symp2002PMC2244554 Proc taxonomy of medication errors AMIA Symp. 2002 : 71–75. PMCID: PMC2244554Evaluating a medical error taxonomy.Juliana Brixey, Todd R. human error taxonomy Johnson, and Jiajie ZhangUniversity of Texas, Health Science Center at Houston, Houston, TX, USA.Author information ► Copyright and License information ►Copyright notice This article
Medication Error Classification
has been cited by other articles in PMC.AbstractHealthcare has been slow in using human factors principles to reduce medical errors. The Center for Devices and Radiological Health (CDRH) recognizes that a lack of attention to human factors during product development may lead to errors that have the ncc merp medication error definition potential for patient injury, or even death. In response to the need for reducing medication errors, the National Coordinating Council for Medication Errors Reporting and Prevention (NCC MERP) released the NCC MERP taxonomy that provides a standard language for reporting medication errors. This project maps the NCC MERP taxonomy of medication error to MedWatch medical errors involving infusion pumps. Of particular interest are human factors associated with medical device errors. The NCC MERP taxonomy of medication errors is limited in mapping information from MEDWATCH because of the focus on the medical device and the format of reporting.Full textFull text is available as a scanned copy of the original print version. Get a printable copy (PDF file) of the complete article (916K), or click on a page image below to browse page by page. Links to PubMed are also available for Sele
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Ncc Merp Taxonomy
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Ismp Medication Error Categories
using your ScienceDirect credentialsUsernamePasswordRemember meForgotten username or password?Sign in via your institutionOpenAthens loginOther institution login Purchase Help Direct export Export file RIS(for EndNote, Reference http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244554/ Manager, ProCite) BibTeX Text RefWorks Direct Export Content Citation Only Citation and Abstract Advanced search JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. This page http://www.sciencedirect.com/science/article/pii/S0925753508000441 uses JavaScript to progressively load the article content as a user scrolls. Click the View full text link to bypass dynamically loaded article content. View full text Safety ScienceVolume 47, Issue 2, February 2009, Pages 227–237 Human error taxonomies applied to driving: A generic driver error taxonomy and its implications for intelligent transport systemsNeville A. Stanton, Paul M. Salmon, Ergonomics Research Group, Brunel University, BIT Lab, School of Engineering and Design, Uxbridge, Middlesex UB8 3PH, UKAvailable online 9 May 2008AbstractRecent research indicates that driver error contributes to up to 75% of all roadway crashes. Despite this, only relatively little is currently known about the types of errors that drivers make and of the causal factors that contribute to these errors being made. This article presents an overview of the literature on human error in road transport. In particular, the work of three pioneers of human error research, Norman, Reason and Rasmussen, is scrutinised. An overview of the research on driv
particular writer types. Alternatively, it can be thought of as derived from analysis of http://www.issco.unige.ch/en/research/projects/ewg95/node125.html texts before and after the notional process of copy-editing, that is, https://www.researchgate.net/publication/223784855_Human_error_taxonomies_applied_to_driving_A_generic_driver_error_taxonomy_and_its_implications_for_intelligent_transport_systems comparison of the unproofed text model and the proofed text model. In both cases, we are interested in errors that actually occur. The purpose of the taxonomy is: To permit the classifying of errors actually encountered in text, to establish classes of frequency and medication error importance, both with respect to writer types and possibly text types, and in aggregate terms; To form the basis of reliable test methods for the performance of systems; To enable the results of testing to be mapped on to the customer-driven presentation of results -- the reportable attributes. Given these purposes, we must establish a taxonomy of error principle to use in classifying grammar errors. The two derivations of the taxonomy we have offered (proofed text plus writer error sources, or proofed text compared with unproofed text) come from different directions, and must be brought together to form a useful classification. If we consider the approach from the proofed text and the sources of writer errors, we could classify errors in terms of their source in the writing process. A few obvious types in this classification, as mentioned in the writer model, would be: slips of medium (typing errors, OCR errors, cut and paste slips...); dialect differences between the writer's language and some standard language; second language errors; concentration lapses resulting in `derailed' sentences; and other performance errors. Such a taxonomy has the advantage that if we have a proper writer model, we cover all errors that result in ungrammatical text, and it may fit the writer's and end-user's categories of thought and thus permit easy mapping on to customer-reportable attribut
Download Full-text PDF Human error taxonomies applied to driving: A generic driver error taxonomy and its implications for intelligent transport systemsArticle (PDF Available) in Safety Science 47(2):227-237 · February 2009 with 197 ReadsDOI: 10.1016/j.ssci.2008.03.006 1st Neville A Stanton42.28 · University of Southampton2nd Paul Matthew Salmon36.14 · University of the Sunshine CoastAbstractRecent research indicates that driver error contributes to up to 75% of all roadway crashes. Despite this, only relatively little is currently known about the types of errors that drivers make and of the causal factors that contribute to these errors being made. This article presents an overview of the literature on human error in road transport. In particular, the work of three pioneers of human error research, Norman, Reason and Rasmussen, is scrutinised. An overview of the research on driver error follows, to consider the different types of errors that drivers make. It was found that all but one of these does not use a human error taxonomy. A generic driver error taxonomy is therefore proposed based upon the dominant psychological mechanisms thought to be involved. These mechanisms are: perception, attention, situation assessment, planning, and intention, memory and recall, and action execution. In addition, a taxonomy of road transport error causing factors, derived from the review of the driver error literature, is also presented. In conclusion to this article, a range of potential technological solutions that could be used to either prevent, or mitigate, the consequences of the driver errors identified are specified.Discover the world's research10+ million members100+ million publications100k+ research projectsJoin for free Full-text (PDF)DOI: ·Available from: Neville A Stanton, Mar 17, 2016 Download Full-text PDF CitationsCitations86ReferencesReferences48The development of the Schema-Action-World (SAW) taxonomy for understanding decision making in aeronautical critical incidents"Alternatively, a pre-existing taxonomy can be applied to relevant data. This approach was taken by Stanton and Sa