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A review by Evans et al reported that 68% of Australian clinical registries routinely undertook some form of data linkage to obtain outcome information, such as death or disease status, and to assess data quality [ 7].
In only two cases in the opt-out arm (none in the opt-in arm) did the fieldworker feel it was inappropriate to discuss data linkage with the participant, and all participants approached about data linkage consented to at least one form of data linkage (3 opt-in, 22 opt-out).
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Previous examples of data linkage projects in North Carolina using data sets were demonstrated.
This study underscores the importance of manual review of data linkages to monitor linkage quality and suggests the need for increased transparency and standardizations in linkage studies.
When this identifier does not exist, some form of data matching or record linkage is required.
This study highlights the potential benefits of further data linkage.
As the number of linkage projects increase, SLUs need robust, efficient methods of managing all forms of data.
Data flows for cross-jurisdictional linkage comprise three distinct phases: Flow of data for linkage Provision of project specific linkage keys Extraction of research data Phase One of the data flow model is about the linkage process.
Figure 1 summarizes the data linkage process and overall proportions of record linkages.
Many countries use some form of linkage procedure to merge data from different sources.
This might take the form of linkage to primary care data to investigate patient's interaction with a GP before an EP.
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