Observational data resources based on the capture of clinical data in the electronic health record (EHR) have produced significant learning opportunities in many areas of medicine. These large data resources can span multiple hospital systems and employ common semantics, ontologies, and data models. They have uncovered critical safety issues for patients, and spurred observational research and clinical decision support. In the age of precision medicine there is also an increased need to obtain genomic and clinical data to discover novel treatments for the deadliest of diseases. With this, there are efforts to create deep-dive disease specific repositories that include tissue in biobanks. The latter require significant human annotation of biospecimens. Securing the data is especially critical in rare pediatric brain tumors. In the specific case of The Children's Brain Tumor Tissue Consortium (CBTTC) an international rare pediatric brain tumor repository, the number of patients that need to be followed prospectively is outpacing the ability of human annotation. In this preliminary study, we perform a prescribed data exploration analysis on simulation data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) employed by the pediatric data network PEDSNet with the intention to ascertain feasibility in automatic annotation of patient records in the CBTTC.