Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction

Sunyang Fu, Lester Y. Leung, Anne Olivia Raulli, David F. Kallmes, Kristin A. Kinsman, Kristoff B. Nelson, Michael S. Clark, Patrick H. Luetmer, Paul R. Kingsbury, David M. Kent, Hongfang Liu

Research output: Contribution to journalArticle

Abstract

Background: The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. However, the validity of EHR-based clinical research is questionable due to poor research reproducibility caused by the heterogeneity and complexity of healthcare institutions and EHR systems, the cross-disciplinary nature of the research team, and the lack of standard processes and best practices for conducting EHR-based clinical research. Method: We developed a data abstraction framework to standardize the process for multi-site EHR-based clinical studies aiming to enhance research reproducibility. The framework was implemented for a multi-site EHR-based research project, the ESPRESSO project, with the goal to identify individuals with silent brain infarctions (SBI) at Tufts Medical Center (TMC) and Mayo Clinic. The heterogeneity of healthcare institutions, EHR systems, documentation, and process variation in case identification was assessed quantitatively and qualitatively. Result: We discovered a significant variation in the patient populations, neuroimaging reporting, EHR systems, and abstraction processes across the two sites. The prevalence of SBI for patients over age 50 for TMC and Mayo is 7.4 and 12.5% respectively. There is a variation regarding neuroimaging reporting where TMC are lengthy, standardized and descriptive while Mayo's reports are short and definitive with more textual variations. Furthermore, differences in the EHR system, technology infrastructure, and data collection process were identified. Conclusion: The implementation of the framework identified the institutional and process variations and the heterogeneity of EHRs across the sites participating in the case study. The experiment demonstrates the necessity to have a standardized process for data abstraction when conducting EHR-based clinical studies.

Original languageEnglish (US)
Article number60
JournalBMC Medical Informatics and Decision Making
Volume20
Issue number1
DOIs
StatePublished - Mar 30 2020

Keywords

  • Clinical research informatics
  • Data quality
  • Electronic health records
  • Learning health system
  • Multi-site studies
  • Reproducibility

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics

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