ArticleJournal of the American Medical Informatics Association : JAMIA2016
A rational approach to legacy data validation when transitioning between electronic health record systems.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transitioning from One Electronic Health Record to Another: A Systematic Review.Journal of general internal medicine · 2023Pooled it
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- Applying requisite imagination to safeguard electronic health record transitions.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Defining Data Migration Across Multidisciplinary Ambulatory Clinics Using Participatory Design.Applied clinical informatics · 2021Review
- Transitions from One Electronic Health Record to Another: Challenges, Pitfalls, and Recommendations.Applied clinical informatics · 2020Article
- Hospital information technology in home care.Experimental and therapeutic medicine · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe objective of this project was to use statistical techniques to determine the completeness and accuracy of data migrated during electronic health record conversion.
methodsData 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.
resultsThe 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.
conclusionsCompared 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.