From Missing To Meaningful: Solving EHR Data Gaps

By Cal Collins

Electronic health records (EHRs) are increasingly recognized as valuable tools in clinical research, offering opportunities to improve patient recruitment, retention, and data collection. EHR-based or pragmatic trials leverage existing clinical data to identify eligible patients, track outcomes, and assess real-world effectiveness. However, the use of EHRs presents a significant challenge: missing or incomplete data. Unstructured data, inconsistent documentation, and provider workflows further compound the problem, with missing values falling into categories such as Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR).

OpenClinica addresses these challenges with adaptive, site-aware EHR-to-EDC mapping, clinically aware data quality logic, and robust site-level oversight. This approach minimizes manual effort, reduces errors, and ensures that relevant data is accurately captured, validated, and contextualized. By enhancing the completeness and reliability of EHR data, OpenClinica enables trials to maintain scientific rigor while accelerating timelines. Ultimately, improving EHR data quality not only strengthens clinical research but also ensures faster access to life-changing therapies for patients in need.