Analysis of data errors in clinical research databases.
S. Goldberg, A. Niemierko, and A. Turchin. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, (January 2008)5488<m:linebreak></m:linebreak>JID: 101209213; OID: NLM: PMC2656002; 2008/03/13 received; 2008/07/01 revised; epublish; SO: AMIA Annu Symp Proc. 2008 Nov 6:242-6.;<m:linebreak></m:linebreak>Recollida de dades.
Abstract
Errors in clinical research databases are common but relatively little is known about their characteristics and optimal detection and prevention strategies. We have analyzed data from several clinical research databases at a single academic medical center to assess frequency, distribution and features of data entry errors. Error rates detected by the double-entry method ranged from 2.3 to 26.9%. Errors were due to both mistakes in data entry and to misinterpretation of the information in the original documents. Error detection based on data constraint failure significantly underestimated total error rates and constraint-based alarms integrated into the database appear to prevent only a small fraction of errors. Many errors were non-random, organized in special and cognitive clusters, and some could potentially affect the interpretation of the study results. Further investigation is needed into the methods for detection and prevention of data errors in research.
5488<m:linebreak></m:linebreak>JID: 101209213; OID: NLM: PMC2656002; 2008/03/13 received; 2008/07/01 revised; epublish; SO: AMIA Annu Symp Proc. 2008 Nov 6:242-6.;<m:linebreak></m:linebreak>Recollida de dades
%0 Journal Article
%1 Goldberg2008
%A Goldberg, Saveli I
%A Niemierko, Andrzej
%A Turchin, Alexander
%D 2008
%J AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
%K ArtificialIntelligence Automated Automated:methods BiomedicalResearch BiomedicalResearch:statistics&numericaldata DataInterpretation Databases Factual MedicalErrors MedicalErrors:prevention&control MedicalErrors:statistics&numericaldata NaturalLanguageProcessing OutcomeAssessment(HealthCare) OutcomeAssessment(HealthCare):methods PatternRecognition Statistical UnitedStates
%P 242-6
%T Analysis of data errors in clinical research databases.
%U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2656002&tool=pmcentrez&rendertype=abstract
%X Errors in clinical research databases are common but relatively little is known about their characteristics and optimal detection and prevention strategies. We have analyzed data from several clinical research databases at a single academic medical center to assess frequency, distribution and features of data entry errors. Error rates detected by the double-entry method ranged from 2.3 to 26.9%. Errors were due to both mistakes in data entry and to misinterpretation of the information in the original documents. Error detection based on data constraint failure significantly underestimated total error rates and constraint-based alarms integrated into the database appear to prevent only a small fraction of errors. Many errors were non-random, organized in special and cognitive clusters, and some could potentially affect the interpretation of the study results. Further investigation is needed into the methods for detection and prevention of data errors in research.
%@ 1942-597X
@article{Goldberg2008,
abstract = {Errors in clinical research databases are common but relatively little is known about their characteristics and optimal detection and prevention strategies. We have analyzed data from several clinical research databases at a single academic medical center to assess frequency, distribution and features of data entry errors. Error rates detected by the double-entry method ranged from 2.3 to 26.9%. Errors were due to both mistakes in data entry and to misinterpretation of the information in the original documents. Error detection based on data constraint failure significantly underestimated total error rates and constraint-based alarms integrated into the database appear to prevent only a small fraction of errors. Many errors were non-random, organized in special and cognitive clusters, and some could potentially affect the interpretation of the study results. Further investigation is needed into the methods for detection and prevention of data errors in research.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Goldberg, Saveli I and Niemierko, Andrzej and Turchin, Alexander},
biburl = {https://www.bibsonomy.org/bibtex/291476e18c282a2e898e60cd95ced2ac9/jepcastel},
city = {Massachusetts General Hospital, Boston, MA, USA.},
interhash = {f49b821bf8272b52213bc6141e0828a8},
intrahash = {91476e18c282a2e898e60cd95ced2ac9},
isbn = {1942-597X},
issn = {1942-597X},
journal = {AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium},
keywords = {ArtificialIntelligence Automated Automated:methods BiomedicalResearch BiomedicalResearch:statistics&numericaldata DataInterpretation Databases Factual MedicalErrors MedicalErrors:prevention&control MedicalErrors:statistics&numericaldata NaturalLanguageProcessing OutcomeAssessment(HealthCare) OutcomeAssessment(HealthCare):methods PatternRecognition Statistical UnitedStates},
month = {1},
note = {5488<m:linebreak></m:linebreak>JID: 101209213; OID: NLM: PMC2656002; 2008/03/13 [received]; 2008/07/01 [revised]; epublish; SO: AMIA Annu Symp Proc. 2008 Nov 6:242-6.;<m:linebreak></m:linebreak>Recollida de dades},
pages = {242-6},
pmid = {18998889},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Analysis of data errors in clinical research databases.},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2656002&tool=pmcentrez&rendertype=abstract},
year = 2008
}