Generally accrued clinical data archived in an electronic health record is captured in a highly dynamic environment where the focus is decidedly on the health of the patient. Additionally, multiple roles and individuals who contribute to a patient's record may enter data differently even when using best practices and solid data standards. Researchers may not be familiar with these issues, as they may be more familiar with highly structured and inflexible IRB protocol data capture methods. The key to finding and resolving these anomalies before they can negatively affect an hypothesis is to understand how and why these events may occur in a clinical care setting in the context of an ever-evolving enterprise EHR.
The data reporting and management group in CBMi consists of a mix of clinical practitioners and data specialists whose dual knowledge of clinical practice and data modeling help to find and understand anomalies. This multidisciplinary team is capable of resolving issues by iteratively tweaking queries or resetting expectations about what data is available for research purposes. The collaboration between the group and the research team is the most critical aspect of finding and minimizing the effects of these anomalies.