A rational approach to legacy data validation when transitioning between electronic health record systems

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Abstract

Objective The objective of this project was to use statistical techniques to determine the completeness and accuracy of data migrated during electronic health record conversion.

Methods Data validation during migration consists of mapped record testing and validation of a sample of the data for completeness and accuracy. We statistically determined a randomized sample size for each data type based on the desired confidence level and error limits.

Results The only error identified in the post go-live period was a failure to migrate some clinical notes, which was unrelated to the validation process. No errors in the migrated data were found during the 12- month post-implementation period.

Conclusions Compared to the typical industry approach, we have demonstrated that a statistical approach to sampling size for data validation can ensure consistent confidence levels while maximizing efficiency of the validation process during a major electronic health record conversion.

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