Regulators in the US and Europe are encouraging novel approaches to expedite and streamline clinical trials. External control arms utilizing Real-World Data (RWD) are an important development, and drug developers are increasingly using external control arms (ECAs) to make internal go/no-go decisions for clinical programs and to support regulatory applications. Randomizing patients into experimental and control arms is critical to building confidence that the benefits observed in patients treated with a drug are due to the drug itself, and not the patients’ baseline characteristics. But the prospect of being assigned to a control group leads many patients to opt out of the clinical trial process all together, and filling control groups in rare and severe diseases is often not feasible or unethical. This short course will go in-depth into how ECAs based on RWD can be designed and executed with a high-standard of data quality and confidence – as a way to decrease trial size, duration, and cost, and incentivize patient participation. In particular, faculty will address how RWD from electronic health records compare to other data sources and how RWD – which goes beyond natural history data because it can include patients undergoing treatment in real time and provides more detailed information at the level of individual patients – can be best utilized in ECAs.