Tumor registries are held to a very high standard for identifying and reporting new analytic cancer cases. However, current approaches to new case detection are often inefficient and costly. Efficient and effective detection of new cancer cases has the potential to maintain a high accuracy of reporting while reducing costs, increasing timeliness of reporting, and ultimately advancing cancer research. We describe the development, implementation, and evaluation of an informatics tool that integrates multiple data sources to support the workflow of new case identification at the Vanderbilt University Medical Center (VUMC) tumor registry office. The new system reduced the total number of potential cases to analyze from roughly 13,000 to 2,500 records per month. This resulted in an efficiency gain of roughly 80 man hours per month with a respective annual savings of approximately 50,000 dollars. Further iterative refinement of this approach along with support for case abstraction could result in further efficiencies.