This document discusses how predictive analytics using real-world data can help identify undiagnosed rare disease patients. It describes two case studies: 1) A screening algorithm identified potentially undiagnosed patients for a rare multi-system disease with a high risk prevalence of 20.5% compared to 0.7% of the population. 2) An analysis of a rare cardiac disease identified health system barriers like variability between diagnostic centers that could cause under diagnosis. While initial results are promising, challenges remain around data privacy, sample size, and clinician adoption of screening algorithms.