Think globablly, act locally: How central cancer registries can be a resource for other disease registries and surveillance activites

BACKGROUND: Cancer registries, particularly central cancer registries (CCRs) with their structured data elements in standardized data layouts, recognized standards for measuring quality, completeness and timeliness and long experience with secure electronic data submission, are powerful tools that have not been utilized to their full potential. One limitation in utilizing different types of registries to assess outcomes and measure quality of care or disparity reduction is that different types of registries do not have a common core set of variables or framework. Registries are usually categorized by the disease they collect (e.g., cancer, stroke, birth defects) and often do not collect information on co-morbidities or disease progression. Lack of interoperability has also been a barrier. OBJECTIVES/PURPOSE: Explore the practicality of using CCRs as a model for other disease registries and as a foundation for integrated registries and disease reporting processes. METHODS: Strengths, including the availability of software, and weaknesses of CCRs as a model will be presented. Commonalities of disease registries will be explored to identify similarities and intersections in the process of disease reporting. The advantages of and need for interoperability will be outlined. RESULTS: We will present a model to expand the scope of CCRs and to interrelate and connect various disease registries. Stage 2 of Meaningful Use is already making a difference to CCRs with the collection of comorbid conditions. We will also discuss how Stage 3 of Meaningful Use represents a step toward interoperability and integration. DISCUSSION/CONCLUSION: In order to be more interoperable, disease registries can benefit from each other while working towards a common data set and interoperability. With inclusion of multiple conditions and diseases, the framework becomes useful for implementation in various settings and is applicable for collection of diverse data in diverse settings. Costs can be reduced and disparity reduction can be facilitied.