Literature DB >> 18390166

Modelling of errors in databases.

Steve Gallivan1, Christina Pagel.   

Abstract

A lot of time and energy are expended assembling national databases containing information about health care processes and outcomes. Unfortunately, given the complexity of the data gathering procedures involved, errors occur. This inevitably leads to problems when it comes to the analysis of data from such sources. Indeed, sometimes it is very much a matter of faith that summary statistics represent a true reflection of the facts. On the assumption that one knows the rates at which different forms of errors occur, mathematical modelling methods can be used to obtain estimates of the effects of such errors on the estimates that would be derived for summary statistics associated with an erroneous data base.

Mesh:

Year:  2008        PMID: 18390166     DOI: 10.1007/s10729-007-9022-y

Source DB:  PubMed          Journal:  Health Care Manag Sci        ISSN: 1386-9620


  11 in total

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2.  Coding errors: a comparative analysis of hospital and prospectively collected departmental data.

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Review 5.  Publishing outcome data: is it an effective approach?

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6.  The effect of misclassification errors on case mix measurement.

Authors:  Jason M Sutherland; Chas K Botz
Journal:  Health Policy       Date:  2006-01-24       Impact factor: 2.980

7.  Robustness of prevalence estimates derived from misclassified data from administrative databases.

Authors:  Martin Ladouceur; Elham Rahme; Christian A Pineau; Lawrence Joseph
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8.  How accurate are hospital discharge data for evaluating effectiveness of care?

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Journal:  Med Care       Date:  1993-08       Impact factor: 2.983

9.  Are diagnosis specific outcome indicators based on administrative data useful in assessing quality of hospital care?

Authors:  I Scott; D Youlden; M Coory
Journal:  Qual Saf Health Care       Date:  2004-02

10.  Validation of diagnostic codes within medical services claims.

Authors:  Machelle Wilchesky; Robyn M Tamblyn; Allen Huang
Journal:  J Clin Epidemiol       Date:  2004-02       Impact factor: 6.437

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  1 in total

1.  Error rates in a clinical data repository: lessons from the transition to electronic data transfer--a descriptive study.

Authors:  Matthew K H Hong; Henry H I Yao; John S Pedersen; Justin S Peters; Anthony J Costello; Declan G Murphy; Christopher M Hovens; Niall M Corcoran
Journal:  BMJ Open       Date:  2013-05-28       Impact factor: 2.692

  1 in total

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