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

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.

Effect of New Nursing on Patients with Acute Cerebral Infarction

“…The neurological function of both groups after the intervention was measured using the National Institute of Health Stroke Scale (NIHSS) [ 19 ], with 11 items and a total score of 42 points. A higher score represented more severe neurological impairment in patients.…”

Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease

“…Characteristics of the cohort were described previously [ 8 ]. The mean ages were 65 (± 10.6) and 66 (± 9.7) for the Mayo and Tufts cohorts.…”

“…Patients older than 50 years with neuroimaging (CT, MRI) between 1/2009 and 10/2015 and no history of stroke, transient ischemic attack (TIA), or dementia were identified at two centers (Mayo Clinic, Tufts Medical Center). 1000 CT and MRI studies (500 each) were randomly selected through a previously described protocol [ 8 ]. The residents and neuroradiologists were instructed on annotation of the presence and characteristics of SBIs and WMD according to a consensus guide developed by two study investigators (LYL, PHL) ( Additional file 1 : Expanded Methods).…”

Assessing quality and agreement of structured data in automatic versus manual abstraction of the electronic health record for a clinical epidemiology study

“…Accuracy of data obtained through automated EHR characterization versus manual searches has been studied in diverse contexts, with heterogeneity of the EHR and variable agreement between abstraction methods being common themes. [4][5][6] Manual EHR abstraction, while considered a gold standard, 7 compares to automated abstraction in its susceptibility to quality issues due to clinical or administrative coding errors and also missing data. However, despite common limitations due to underlying data quality, potential advantages of automated, standardized search techniques are numerous, including more timely results, reduced labor intensity, reduced likelihood of data entry errors, unbiased queries, larger sample sizes, and capacity to define search parameter ranges precisely.…”