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Keywords: clinical decision support, data science, precision medicine, oncology,data warehouse
BACKGROUND: The objective of the Miami Cancer Institute Oncology Data Warehouse (ODW) is to collect and organize data from clinical records, research, and administrative systems to support information retrieval, business intelligence, and analytics for high-level decision making for oncology patients. The design, architecture, and implementation aligns with industry best practices. METHODS: The ODW is modeled as a star schema, with fact tables and conformed dimension tables, and expands to a galaxy schema with constellation facts and dimensions that can snowflake to other data models. Each fact table represents a subject area (i.e. pathology) related to the conformed dimension tables using surrogate and foreign keys. Conformed dimensions are attributes associated to the subject area (i.e. date of encounter). The source data is extracted, transformed and loaded (ETL) automatically from different systems into a set of related tables. Data incrementally loads at prescribed intervals into two parallel storage areas, a relational database management system and Big Data file storage system. RESULTS: An interdisciplinary team, has designed, developed, and implemented the ODW with information originating from legacy and current data sources which include: electronic medical record (EMR) systems, financial systems, clinical trial management systems, tumor registries, biospecimen repository, pathology synoptic reports, and Next Generation Sequencing services. The ODW is capable of connecting most business intelligence or statistical tools for automated or static report development. CONCLUSION: By implementing an innovative combination of technology tools and methods, we were able to organize enterprise information about oncology patients which can be utilized for clinical decision support and precision medicine use cases enabling us to systematically mine and review information of patients, and identify patterns that may influence treatment decisions and potential outcomes.