Real-world evidence is being used increasingly to provide important information about natural history of a disease, drug utilization and treatment patterns, comorbidities, safety and effectiveness of medications, along with healthcare utilization, cost of care, and other economic analyses. Administrative databases are a cornerstone of real-world evidence, offering real-time data for large populations in which to study rare diseases and sensitive subgroups. While bringing unparalleled scope, large claims databases can lack the clinical depth needed to address certain challenges, while electronic medical record databases can have significant missing data limitations.
Rapid advances in precision medicine are revealing many diseases to be heterogeneous entities with different etiologies and natural histories. This understanding is opening doors for the development of more effective targeted treatments. For example, over the past decade, breast cancer therapies have been approved for and successfully treated specific forms of the disease defined by molecular profiling. As targeted treatments proliferate, there is a growing need to better understand smaller subpopulations with more narrowly defined conditions of interest.
Administrative claims databases contain longitudinal healthcare information on all billed healthcare encounters on the scale needed to effectively study targeted indications. Rapidly evolving coding systems contain a plethora of diagnosis codes, yet lag the development of clinical information like staging, and molecular and genomic profiling. This situation presents a challenge to researchers trying to unlock the potential of automated databases to support the moving target of therapeutic advances.