Data Triangulation to Explore the Information Value in Different Healthcare Data Sources

This research position requires literature review, data analysis, visualization, and interpretation with a focus on integrating different healthcare data sources (i.e., data triangulation) to better understand how/when/where diagnostic error occur in primary care setting. Specifically, the tasks include literature review on diagnostic error in primary care related to type 2 diabetes diagnosis and data triangulation methods to integrate and interpret different healthcare data sources in this context. Data sources in this study include individual-level Electronic Health Records (EHR) data – including visit, labs, vital, diagnosis and socio-demographic factors- provided by a healthcare delivery system, combined with chart review data completed by providers for selected patients. Descriptive analysis and data visualization methods aim to explore the information value of chart review data in providing the context missing in EHR data. The student will report to Dr. Muge Capan on a weekly basis via email and provide updates on the weekly tasks via MS Teams meetings to the larger research team. Final deliverables include a project report and a manuscript draft to be submitted to an appropriate journal.