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

Autor: Michael S. Clark, David F. Kallmes, Kristin A. Kinsman, Hongfang Liu, Paul R. Kingsbury, Sunyang Fu, Anne Olivia Raulli, Lester Y. Leung, Kristoff B. Nelson, Patrick H. Luetmer, David M. Kent
Rok vydání: 2020
Předmět:
Brain Infarction
Male
Best practice
Health Informatics
lcsh:Computer applications to medicine. Medical informatics
Health informatics
03 medical and health sciences
0302 clinical medicine
Documentation
Knowledge extraction
health services administration
Health care
Humans
Electronic health records
Medicine
030212 general & internal medicine
health care economics and organizations
Aged
Aged
80 and over
Data collection
Clinical research informatics
business.industry
Research
Health Policy
Data quality
Reproducibility of Results
Learning health system
Middle Aged
medicine.disease
Reproducibility
3. Good health
Computer Science Applications
Multi-site studies
Clinical research
lcsh:R858-859.7
Female
Medical emergency
business
Delivery of Health Care
030217 neurology & neurosurgery
Research Article
Zdroj: BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-12 (2020)
BMC Medical Informatics and Decision Making
ISSN: 1472-6947
Popis: 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.
Databáze: OpenAIRE
Externí odkaz: