Caisis is a web-based database system for managing clinical research and patient care data. We created Caisis to address the lack of minimally biased database systems capable of efficiently managing large, reusable datasets to support predictive modeling and translational research. It was primarily developed by our team at Memorial Sloan-Kettering Cancer Center and is currently operating at multiple institutions. Our central, long-term objective is to make the Caisis system more adaptable to other groups and institutions.
Our specific aims will make Caisis extendable by restructuring the Caisis data model to accommodate other diseases, migrating existing prediction tools and algorithms into the Caisis framework, making Caisis compatible with the NCI's Cancer Biomedical Informatics Grid (caBIG), and adding support for international users. Additionally, we will continue to make Caisis easier to use by supporting the collaborative open source development of the system, improving and expanding Caisis documentation, and updating the system administration interface. Finally, we will make Caisis more scalable, compatible with web browsers for Macintosh and Linux operating systems, and portable to free open source platforms. These changes will make Caisis more valuable to the scientific community by allowing users to customize Caisis to their clinical and research priorities and by promoting multi-institutional collaboration. As more institutions adopt Caisis and the system is migrated towards national standards, it will be easier for researchers to compile large, multi-institutional datasets with sufficient sample-size power to answer questions pertaining to specific patient populations. The proposed improvements also will help the Caisis community integrate with national efforts to create interoperable information systems, enlarging the potential collaborative research community and further propagating systems that advance cancer research and patient care. ? ? ?
