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296 Background: Quantifying the performance of a cancer center’s cancer registry is important to plan for resources needed to address shortcomings and projected increases in workload. The objective of this project was to understand the workflow, reporting requirements, and existing documentation of the cancer registry at Dana-Farber Cancer Institute (DFCI) in order to build a dashboard that quantifies monthly performance. Methods: Current state workflow maps were created detailing the three phases of the cancer registry’s operations: case finding, case abstraction, and case follow-up. Individual meetings and a workshop with the cancer registry staff were held to validate workflows, gather operational challenges and improvement ideas, and set performance metrics. Leveraging information already being collected by staff as part of their workflow, a database was designed to collect and auto-visualize monthly performance using Excel and Tableau. Results: The performance dashboard shows monthly trends of ‘on target’ status of the cancer registry’s case finding, case abstraction, and case follow-up efforts. It also quantifies how delayed the cancer registry is in case finding or abstraction and provides new case count trends by care facility, staffing capacity based on case load, and trends in lost to follow-up patients. One hundred percent of the performance dashboard data is from documentation already generated by the cancer registry staff on a routine basis. Data on monthly case load from September 2017 through March 2018 are presented in the table below; given a maximum limit of 86 cases to abstract per abstractor per month, the cancer registry is currently at more than full capacity. Conclusions: A well-designed cancer registry performance dashboard that leverages existing data does not add burden to registry staff and informs how best to manage and utilize resources according to case load. [Table: see text]

212 Background: The ability to link cancer registry data to clinical and administrative data sets for quality improvement has long been desired. We sought to integrate registry data into a central data warehouse in an effort to make available for the first time consistent and reliable diagnosis and staging data to a broad hospital user group. Methods: After a short period of data analysis, the tables for Cancer Registry data (Oracle) were modeled. The source data (SQL Server) was conformed and integrated using an ETL tool (Informatica). All ETL QA work was performed with SQL queries. Cancer registry data was integrated into reporting architecture (Microstrategy) to facilitate design of standardized and ad hoc reports. Results: 140 distinct fields on demographics, staging (clinical, pathological, collaborative), site specific categories, diagnosis, and treatment were integrated into the Dana-Farber Analytics Reporting Tool (DART) for historic Cancer Registry data beginning with January 2010 newly diagnosed cancers. All Cancer Registry data and patient files (new and old) are updated in DART on a monthly basis. Conclusions: Individuals across the hospital now have the ability to link clinical and administrative data from our EMR, institutional QI data from varied systems, and pharmacy data to Cancer Registry data in the DART tool. One example of the integration of these multiple data sets is the linkage of staging data from the Cancer Registry data set and time to referral data from the administrative data set by patient MRN. As DFCI aims to cohort its patients based on their primary diagnosis for quality improvement and other internal reporting needs, the ability to analyze patients in this way becomes critical. This project sets an example for other centers as they integrate Cancer Registry data into user friendly business intelligence systems to help meet federal reporting mandates and aid internal improvement work. [Table: see text]