Classifying, measuring and improving the quality of data in trauma registries

A review of the literature

Research output: Contribution to journalReview ArticleResearchpeer-review

18 Citations (Scopus)

Abstract

Introduction: Globally, injury is a major cause of death and disability. Improvements in trauma care have been driven by trauma registries. The capacity of a trauma registry to inform improvements in the quality of trauma care is dependent upon the quality of data. The literature on data quality in disease registries is inconsistent and ambiguous; methods used for classifying, measuring, and improving data quality are not standardised. The aim of this study was to review the literature to determine the methods used to classify, measure and improve data quality in trauma registries. Methods: A scoping review of the literature was performed. Databases were searched using the term ''trauma registry'' and its synonyms, combined with multiple terms denoting data quality. There was no restriction on year. Full-length manuscripts were included if the classification, measurement or improvement of data quality in one or more trauma registries was a study objective. Data were abstracted regarding registry demographics, study design, data quality classification, and the reported methods used to measure and improve the pre-defined data quality dimensions of accuracy, completeness and capture. Results: Sixty-nine publications met the inclusion criteria. Four publications classified data quality. The most frequently described methods for measuring data accuracy (n = 47) were checks against other datasets (n = 18) and checks of injury coding (n = 17). The most frequently described methods for measuring data completeness (n = 47) were the percentage of included cases, for a given variable or list of variables, for which there was an observation in the registry (n = 29). The most frequently described methods for measuring data capture (n = 37) were the percentage of cases in a linked reference dataset that were also captured in the primary dataset being evaluated (n = 24). Most publications dealing with the measurement of a dimension of data quality did not specify the methods used; most publications dealing with the improvement of data quality did not specify the dimension being targeted. Conclusion: The classification, measurement and improvement of data quality in trauma registries is inconsistent. To maintain confidence in the usefulness of trauma registries, the metrics and reporting of data quality need to be standardised. Crown .

Original languageEnglish
Pages (from-to)559-567
Number of pages9
JournalInjury
Volume47
Issue number3
DOIs
Publication statusPublished - 2016

Keywords

  • Accuracy
  • Capture
  • Completeness
  • Data quality
  • Injury databank
  • Injury database
  • Injury registry
  • Trauma databank
  • Trauma database
  • Trauma registry

Cite this

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title = "Classifying, measuring and improving the quality of data in trauma registries: A review of the literature",
abstract = "Introduction: Globally, injury is a major cause of death and disability. Improvements in trauma care have been driven by trauma registries. The capacity of a trauma registry to inform improvements in the quality of trauma care is dependent upon the quality of data. The literature on data quality in disease registries is inconsistent and ambiguous; methods used for classifying, measuring, and improving data quality are not standardised. The aim of this study was to review the literature to determine the methods used to classify, measure and improve data quality in trauma registries. Methods: A scoping review of the literature was performed. Databases were searched using the term ''trauma registry'' and its synonyms, combined with multiple terms denoting data quality. There was no restriction on year. Full-length manuscripts were included if the classification, measurement or improvement of data quality in one or more trauma registries was a study objective. Data were abstracted regarding registry demographics, study design, data quality classification, and the reported methods used to measure and improve the pre-defined data quality dimensions of accuracy, completeness and capture. Results: Sixty-nine publications met the inclusion criteria. Four publications classified data quality. The most frequently described methods for measuring data accuracy (n = 47) were checks against other datasets (n = 18) and checks of injury coding (n = 17). The most frequently described methods for measuring data completeness (n = 47) were the percentage of included cases, for a given variable or list of variables, for which there was an observation in the registry (n = 29). The most frequently described methods for measuring data capture (n = 37) were the percentage of cases in a linked reference dataset that were also captured in the primary dataset being evaluated (n = 24). Most publications dealing with the measurement of a dimension of data quality did not specify the methods used; most publications dealing with the improvement of data quality did not specify the dimension being targeted. Conclusion: The classification, measurement and improvement of data quality in trauma registries is inconsistent. To maintain confidence in the usefulness of trauma registries, the metrics and reporting of data quality need to be standardised. Crown .",
keywords = "Accuracy, Capture, Completeness, Data quality, Injury databank, Injury database, Injury registry, Trauma databank, Trauma database, Trauma registry",
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year = "2016",
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Classifying, measuring and improving the quality of data in trauma registries : A review of the literature. / O'Reilly, Gerard M.; Gabbe, Belinda; Moore, Lynne; Cameron, Peter A.

In: Injury, Vol. 47, No. 3, 2016, p. 559-567.

Research output: Contribution to journalReview ArticleResearchpeer-review

TY - JOUR

T1 - Classifying, measuring and improving the quality of data in trauma registries

T2 - A review of the literature

AU - O'Reilly, Gerard M.

AU - Gabbe, Belinda

AU - Moore, Lynne

AU - Cameron, Peter A.

PY - 2016

Y1 - 2016

N2 - Introduction: Globally, injury is a major cause of death and disability. Improvements in trauma care have been driven by trauma registries. The capacity of a trauma registry to inform improvements in the quality of trauma care is dependent upon the quality of data. The literature on data quality in disease registries is inconsistent and ambiguous; methods used for classifying, measuring, and improving data quality are not standardised. The aim of this study was to review the literature to determine the methods used to classify, measure and improve data quality in trauma registries. Methods: A scoping review of the literature was performed. Databases were searched using the term ''trauma registry'' and its synonyms, combined with multiple terms denoting data quality. There was no restriction on year. Full-length manuscripts were included if the classification, measurement or improvement of data quality in one or more trauma registries was a study objective. Data were abstracted regarding registry demographics, study design, data quality classification, and the reported methods used to measure and improve the pre-defined data quality dimensions of accuracy, completeness and capture. Results: Sixty-nine publications met the inclusion criteria. Four publications classified data quality. The most frequently described methods for measuring data accuracy (n = 47) were checks against other datasets (n = 18) and checks of injury coding (n = 17). The most frequently described methods for measuring data completeness (n = 47) were the percentage of included cases, for a given variable or list of variables, for which there was an observation in the registry (n = 29). The most frequently described methods for measuring data capture (n = 37) were the percentage of cases in a linked reference dataset that were also captured in the primary dataset being evaluated (n = 24). Most publications dealing with the measurement of a dimension of data quality did not specify the methods used; most publications dealing with the improvement of data quality did not specify the dimension being targeted. Conclusion: The classification, measurement and improvement of data quality in trauma registries is inconsistent. To maintain confidence in the usefulness of trauma registries, the metrics and reporting of data quality need to be standardised. Crown .

AB - Introduction: Globally, injury is a major cause of death and disability. Improvements in trauma care have been driven by trauma registries. The capacity of a trauma registry to inform improvements in the quality of trauma care is dependent upon the quality of data. The literature on data quality in disease registries is inconsistent and ambiguous; methods used for classifying, measuring, and improving data quality are not standardised. The aim of this study was to review the literature to determine the methods used to classify, measure and improve data quality in trauma registries. Methods: A scoping review of the literature was performed. Databases were searched using the term ''trauma registry'' and its synonyms, combined with multiple terms denoting data quality. There was no restriction on year. Full-length manuscripts were included if the classification, measurement or improvement of data quality in one or more trauma registries was a study objective. Data were abstracted regarding registry demographics, study design, data quality classification, and the reported methods used to measure and improve the pre-defined data quality dimensions of accuracy, completeness and capture. Results: Sixty-nine publications met the inclusion criteria. Four publications classified data quality. The most frequently described methods for measuring data accuracy (n = 47) were checks against other datasets (n = 18) and checks of injury coding (n = 17). The most frequently described methods for measuring data completeness (n = 47) were the percentage of included cases, for a given variable or list of variables, for which there was an observation in the registry (n = 29). The most frequently described methods for measuring data capture (n = 37) were the percentage of cases in a linked reference dataset that were also captured in the primary dataset being evaluated (n = 24). Most publications dealing with the measurement of a dimension of data quality did not specify the methods used; most publications dealing with the improvement of data quality did not specify the dimension being targeted. Conclusion: The classification, measurement and improvement of data quality in trauma registries is inconsistent. To maintain confidence in the usefulness of trauma registries, the metrics and reporting of data quality need to be standardised. Crown .

KW - Accuracy

KW - Capture

KW - Completeness

KW - Data quality

KW - Injury databank

KW - Injury database

KW - Injury registry

KW - Trauma databank

KW - Trauma database

KW - Trauma registry

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DO - 10.1016/j.injury.2016.01.007

M3 - Review Article

VL - 47

SP - 559

EP - 567

JO - Injury

JF - Injury

SN - 0020-1383

IS - 3

ER -