Natalie M Pageler1, Max Jacob Grazier G'Sell2, Warren Chandler3, Emily Mailes3, Christine Yang3, Christopher A Longhurst4. 1. Division of Critical Care Medicine Department of Pediatrics, Stanford University School of Medicine Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA Information Services Department, Stanford Children's Health, CA npageler@stanford.edu. 2. Department of Statistics, Carnegie Mellon University, Pittsburgh, PA. 3. Information Services Department, Stanford Children's Health, CA. 4. Departments of Biomedical Informatics and Pediatrics, University of California San Diego, CA.
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.
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.
Authors: Chunya Huang; Ross Koppel; John D McGreevey; Catherine K Craven; Richard Schreiber Journal: Appl Clin Inform Date: 2020-11-11 Impact factor: 2.342