While evolutionary approaches to medicine show promise, measuring evolution itself is difficult due to experimental constraints and the dynamic nature of body systems. In cancer evolution, continuous observation of clonal architecture is impossible, and longitudinal samples from multiple timepoints are rare. Increasingly available DNA sequencing datasets at single cell resolution enable the reconstruction of past evolution using mutational history, allowing for a better understanding of dynamics prior to detectable disease. We derive methods based on coalescent theory for estimating the net growth rate of clones from either reconstructed phylogenies or the number of shared mutations. Using single-cell datasets from blood, we apply and validate our analytical methods for estimating the net growth rate of hematopoietic clones, eliminating the need for complex simulations. We show that our estimates may have broad applications to improve mechanistic understanding and prognostic ability. Compared to clones with a single or unknown driver mutation, clones with multiple drivers have significantly increased growth rates (median 0.94 vs. 0.25 per year; p = 1.6 x 10-6). Further, stratifying patients with a myeloproliferative neoplasm (MPN) by the growth rate of their fittest clone shows that higher growth rates are associated with shorter time from clone initiation to MPN diagnosis (median 13.9 vs. 26.4 months; p = 0.0026)