Abstract
BackgroundThe 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.MethodWe 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.ResultWe 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.ConclusionThe 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.
Highlights
The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery
The implementation of the framework identified the institutional and process variations and the heterogeneity of EHRs across the sites participating in the case study
At Mayo, 262,061 reports were obtained from Mayo EHR based on the CPT inclusion criteria. 4015 reports were randomly sampled for cohort screening. 749 were eligible for annotation after applying the International Classification of Diseases (ICD) exclusion criteria
Summary
The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. A continuously learning health system can enable efficient and effective care delivery with the ability to discover practice-based knowledge and the seamless integration of clinical research with care practice [2, 3]. To achieve such a vision, it is critical to have a robust data and informatics infrastructure with the following properties: 1) high-throughput and real-time methods for data extraction and analysis, 2) transparent and reproducible processes to ensure scientific rigor in clinical research, and 3) implementable and generalizable scientific findings [1, 2, 4,5,6,7]. The development and evaluation of NLP algorithms for a specific chart review task requires the manual creation of a gold standard clinical corpus, there is a lack of standard processes or best practices for creating such a corpus [19, 20]
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