Background: Clinical information management is a burdensome process for oncology providers owing to the complexity of modern cancer data. The results that a clinician needs to review are often spread across several areas within the EHR, making it cumbersome to sense a broad overview of patient status. The purpose of this study is to describe a framework for oncologist-driven development of a data visualization tool to trend cancer biomarkers and assess the feasibility of this tool to query and display EHR data relevant for the treatment of prostate cancer. Methods: A clinical sponsor is selected to identify data elements necessary to make treatment decisions for patients receiving therapy for prostate cancer and to provide rapid feedback for the clinical tool interface. The commercial EHR database is queried to determine identifying codes for relevant laboratory tests, medications, and procedures. Data elements are assembled using the EHR platform for clinical synopsis and the clinical tool is made available to five genitourinary medical oncologists for initial pilot. Results: Oral prostate cancer medications were queried based on medication therapeutic and pharmaceutical class. In addition, androgen deprivation therapy (ADT) injections were separately identified based on route of administration. Prostate specific antigen (PSA) and testosterone result values were queried using laboratory base and/or common name codes. However, many duplicate entities are found varying by hospital/laboratory site and test assay. The assembled clinical data visualization tool can overlay temporal trends in PSA and testosterone over medication start/stop dates to convey treatment response and signs of early medication resistance. The clinician also has the option to overlay vital signs or other laboratory information to visualize treatment related adverse events (ex. weight gain related to ADT, anemia related to PARP inhibitor therapy, etc.) Lastly, the tool can also highlight the dates of the patient’s last imaging tests to allow clinicians to determine if the patient is due for any follow up imaging. Conclusions: The EHR can support novel data visualization tools for cancer biomarkers that can reasonably support clinical workflows. Development requires an intimate knowledge of EHR data but may still be limited by duplicate or erroneous codes for laboratory results. This issue may be addressed by using standard nomenclature for laboratory results such as LOINC codes but is not currently supported by the commercial EHR. Future work in this area will include formal usability testing from the perspective of oncology providers and patients.