Database Error Rates
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Help Latest content Archive eLetters RSS Home > Volume 3, Issue 5 > Article BMJ Open 2013;3:e002406 doi:10.1136/bmjopen-2012-002406 Health informatics Research Error rates in medication error rates a clinical data repository: lessons from the transition to electronic data transfer—a
Error Rates For Predicting Binary Variables
descriptive study Matthew K H Hong1, Henry H I Yao1, John S Pedersen2, Justin S Peters1, Anthony
Error Rates Statistics
J Costello1, Declan G Murphy1,3, Christopher M Hovens1, Niall M Corcoran1 1Division of Urology, Department of Surgery, University of Melbourne, Royal Melbourne Hospital and the Australian Prostate Cancer Research
Error Rates Should Be Measured When The Network Traffic ________
Centre Epworth, Melbourne, Victoria, Australia 2TissuPath Specialist Pathology, Mount Waverley and Monash University Faculty of Medicine, Melbourne, Victoria, Australia 3Division of Cancer Surgery, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia Correspondence to Dr Matthew Hong; m.k.hong{at}ausdoctors.net Received 26 November 2012 Accepted 9 April 2013 Published 17 May 2013 Next Section Abstract Objective Data errors are a well-documented average data entry error rate part of clinical datasets as is their potential to confound downstream analysis. In this study, we explore the reliability of manually transcribed data across different pathology fields in a prostate cancer database and also measure error rates attributable to the source data. Design Descriptive study. Setting Specialist urology service at a single centre in metropolitan Victoria in Australia. Participants Between 2004 and 2011, 1471 patients underwent radical prostatectomy at our institution. In a large proportion of these cases, clinicopathological variables were recorded by manual data-entry. In 2011, we obtained electronic versions of the same printed pathology reports for our cohort. The data were electronically imported in parallel to any existing manual entry record enabling direct comparison between them. Outcome measures Error rates of manually entered data compared with electronically imported data across clinicopathological fields. Results 421 patients had at least 10 comparable pathology fields between the electronic import and manual records and were selected for study. 320 patients had concordant data between manually entered and electronically populated fields in a median of
Health Search databasePMCAll DatabasesAssemblyBioProjectBioSampleBioSystemsBooksClinVarCloneConserved DomainsdbGaPdbVarESTGeneGenomeGEO DataSetsGEO ProfilesGSSGTRHomoloGeneMedGenMeSHNCBI Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedPubMed HealthSNPSRAStructureTaxonomyToolKitToolKitAllToolKitBookToolKitBookghUniGeneSearch termSearch Advanced Journal list Help Journal ListPLoS ONEv.3(8); 2008PMC2516178 typical data entry error rates PLoS ONE. 2008; 3(8): e3049. Published online 2008 Aug 25. doi: human error rate statistics 10.1371/journal.pone.0003049PMCID: PMC2516178Quantifying Data Quality for Clinical Trials Using Electronic Data CaptureMeredith L. Nahm,1,* Carl F. Pieper,2 and data entry error rate calculation Maureen M. Cunningham3Roberta W. Scherer, Editor1Duke Translational Medicine Institute, Durham, North Carolina, United States of America2Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United http://bmjopen.bmj.com/content/3/5/e002406.full States of America3Duke Clinical Research Institute, Durham, North Carolina, United States of AmericaJohns Hopkins Bloomberg School of Public Health, United States of America* E-mail: ude.ekud@mhan.htideremConceived and designed the experiments: MLN CFP. Performed the experiments: CFP. Analyzed the data: MLN CFP. Wrote the paper: MLN CFP MMC.Author information ► Article notes ► Copyright and License information ►Received http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2516178/ 2008 Mar 5; Accepted 2008 Aug 4.Copyright This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.This article has been cited by other articles in PMC.AbstractBackgroundHistorically, only partial assessments of data quality have been performed in clinical trials, for which the most common method of measuring database error rates has been to compare the case report form (CRF) to database entries and count discrepancies. Importantly, errors arising from medical record abstraction and transcription are rarely evaluated as part of such quality assessments. Electronic Data Capture (EDC) technology has had a further impact, as paper CRFs typically leveraged for quality measurement are not used in EDC processes.Methods and Principal FindingsThe National Institute on Drug Abuse Treatment Clinical Trials Network has developed, implemented, and evaluated methodology for holistically assessing data quality on EDC tr
Event Operations Management Event Inventory Management Audit Ready Event Financials Venue Reporting Mobile Venue Management Venue Websites Event Products Ungerboeck for Exhibitions Exhibitor Sales CRM https://ungerboeck.com/blog/when-good-info-goes-bad-the-real-cost-of-human-data-errors-part-1-of-2 Exhibition Management Exhibition Floor Plan Management Audit Ready Event Financials & Accounting Reporting Trade Show Websites Ungerboeck for Conferences Events & Conference CRM Event Registration Event Management Conference Websites http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-170 Event Accounting Event Reporting Websites for Events Mobile Attendee App Client Services Professional Services Client Care Cloud Hosting Upcoming Training About News Events Contact Us Leadership Team Manish Chandak error rate Shannon Wilson Dale Overton Dieter Ungerboeck Careers EBMS Resources Blog Conference Download Overview Request a Demo When Good Info Goes Bad: The Real Cost of Human Data Errors – Part 1 of 2 Home>Blog>When Good Info Goes Bad: The Real Cost of Human Data Errors – Part 1 of 2 Matt Harris 19 May 2014 At 2:45 pm data entry error on May 6, 2010, Wall Street essentially had a heart attack. In just minutes, the stock market plunged 1000 points, for reasons traders, analysts, and business media could not explain. The “flash crash” wiped out $1.1 Trillion of investor dollars and even though most of that was quickly regained, it left the market badly shaken. What happened? It appears that a single keystroke error was to blame. The letter “B” was inserted in a sell order instead of the letter “M”. Billion was input where Million should have been and it triggered a ripple effect through the automated financial markets. Costly errors in the events business might not have as many zeros as that epic fail, but when it’s your event or your exhibitor who has to deal with a problem caused by a keystroke mistake, it can seem just as bad. Today a surprising amount of venue managers and event organizers still work with separate CRM, operations, and financial systems that either require them to manually enter data multiple times, or
Access Estimating the annotation error rate of curated GO database sequence annotationsCraigEJones1, 2Email author, AlfredLBrown1 and UteBaumann2BMC Bioinformatics20078:170DOI: 10.1186/1471-2105-8-170© Jones et al; licensee BioMed Central Ltd.2007Received: 06December2006Accepted: 22May2007Published: 22May2007 Abstract Background Annotations that describe the function of sequences are enormously important to researchers during laboratory investigations and when making computational inferences. However, there has been little investigation into the data quality of sequence function annotations. Here we have developed a new method of estimating the error rate of curated sequence annotations, and applied this to the Gene Ontology (GO) sequence database (GOSeqLite). This method involved artificially adding errors to sequence annotations at known rates, and used regression to model the impact on the precision of annotations based on BLAST matched sequences. Results We estimated the error rate of curated GO sequence annotations in the GOSeqLite database (March 2006) at between 28% and 30%. Annotations made without use of sequence similarity based methods (non-ISS) had an estimated error rate of between 13% and 18%. Annotations made with the use of sequence similarity methodology (ISS) had an estimated error rate of 49%. Conclusion While the overall error rate is reasonably low, it would be prudent to treat all ISS annotations with caution. Electronic annotators that use ISS annotations as the basis of predictions are likely to have higher false prediction rates, and for this reason designers of these systems should consider avoiding ISS annotations where possible. Electronic annotators that use ISS annotations to make predictions should be viewed sceptically. We recommend that curators thoroughly review ISS annotations before accepting them as valid. Overall, users of curated sequence annotations from the GO database should feel assured that they are using a comparatively high quality source of information. BackgroundA major challenge facing bioinformatics today is how to effectively annotate an exponentially increasing body of