  Healthcare data is increasing in size at an unprecedented speed with much
attention on big data analysis and Artificial Intelligence application for
quality assurance, clinical training, severity triaging, and decision support.
Radiology is well-suited for innovation given its intrinsically paired
linguistic and visual data. Previous attempts to unlock this information
goldmine were encumbered by heterogeneity of human language, proprietary search
algorithms, and lack of medicine-specific search performance matrices. We
present a de novo process of developing a document-based, secure, efficient,
and accurate search engine in the context of Radiology. We assess our
implementation of the search engine with comparison to pre-existing manually
collected clinical databases used previously for clinical research projects in
addition to computational performance benchmarks and survey feedback. By
leveraging efficient database architecture, search capability, and clinical
thinking, radiologists are at the forefront of harnessing the power of
healthcare data.
