InfoFrame — An intelligent informatics data collection framework

Health care providers, clinicians and medical researchers are increasingly faced with heterogeneous data collection and reporting standards from a wide variety of public and private organizations. The storing and categorizing of clinical data related from multiple sources not only leads to several heterogeneous databases, but also results in difficulties in automating analysis on gathered datasets. In this work, a flexible real-time data collection framework that is able to adapt and lend itself to the multiple datasets without compromising future usability and research potential is presented. Features include the ability to easily connect and leverage current database systems with legacy data via bridging technologies, auto-complete of lookup listings from external sources (such as medication and physician repositories) and strict data validation on all data entry fields.