Background: The biopharmaceutical industry is evolving such that clinical development is moving from large, well-defined patient populations and highly prevalent disease areas to niche populations and new and rare diseases, making efficient design and recruitment critical. Furthermore, underperforming study sites and unanticipated enrollment and retention challenges compromise the success of clinical trials and other prospective studies. Electronic medical records (EMR), healthcare claims, hospital, and prescription drug data have the potential to guide and refine study protocol design and to inform targeted site selection to drive more efficient study execution. The utility of these data can be further enhanced through the use of both simple and advanced analytics. Methods: Through selected case studies, the use of real-world data to assess protocol feasibility and improve study site selection is illustrated. Results: In a study of respiratory syncytial virus (RSV), real-world data were used to narrow down the RSV patient population and understand RSV care in order to shape future clinical studies. A new patient sub-population was identified, and market sizes of previously identified patient sub-populations were estimated; physician profiles were defined in order to identify potential investigators for future studies. In a global study of irritable bowel disease, real-world claims and EMR data were used to quantify current treatment flow and identify key leverage points for use in recruiting optimal clinical trial patients and sites. Finally, nationally representative EMR data were utilized to highlight clusters of eligible patients within or near currently recruiting study sites, and to provide insights on eligible patients at potential new sites to assess efficacy of a new treatment for moderate to severe rheumatoid arthritis. Conclusion: Insights from real-world data can inform targeted and more efficient clinical trials and prospective observational studies. Such efforts have the potential to reduce the time and cost associated with these studies.