Axon registry® data validation accuracy assessment of data extraction and measure specification

Christine M. Baca, Sarah Benish, Aleksandar Videnovic, Karen Lundgren, Brandon Magliocco, Becky Schierman, Laura Palmer, Lyell K. Jones

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Objective To conduct a data validation study encompassing an accuracy assessment of the data extraction process for the Axon Registry®. Methods Data elements were abstracted from electronic health records (EHRs) by an external auditor (IQVIA) using virtual site visits at participating sites. IQVIA independently calculated Axon Registry quality measure performance rates based on American Academy of Neurology measure specifications and logic using Axon Registry data. Agreement between Axon Registry and IQVIA data elements and measure performance rates was calculated. Discordance was investigated to elucidate underlying systemic or idiosyncratic reasons for disagreement. Results Nine sites (n = 720 patients; n = 80 patients per site) with diversity among EHR vendor, practice settings, size, locations, and data transfer method were included. There was variable concordance between the data elements in the Axon Registry and those abstracted independently by IQVIA; high match rates (≥92%) were observed for discrete elements (e.g., demographics); lower match rates (<44%) were observed for elements with free text (e.g., plan of care). Across all measures, there was a 76% patient-level measure performance agreement between Axon Registry and IQVIA (κ = 0.53, p < 0.001). Conclusion There was a range of concordance between data elements and quality measures in the Axon Registry and those independently abstracted and calculated by an independent vendor. Validation of data and processes is important for the Axon Registry as a clinical quality data registry that utilizes automated data extraction methods from the EHR. Implementation of remediation strategies to improve data accuracy will support the ability of the Axon Registry to perform accurate quality reporting.

Original languageEnglish (US)
Pages (from-to)847-858
Number of pages12
JournalNeurology
Volume92
Issue number18
DOIs
StatePublished - Apr 30 2019

PubMed: MeSH publication types

  • Journal Article
  • Validation Study

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    Baca, C. M., Benish, S., Videnovic, A., Lundgren, K., Magliocco, B., Schierman, B., Palmer, L., & Jones, L. K. (2019). Axon registry® data validation accuracy assessment of data extraction and measure specification. Neurology, 92(18), 847-858. https://doi.org/10.1212/WNL.0000000000007404