Methods that address data shifts usually assume full access to multiple datasets. In the healthcare domain, however, privacy-preserving regulations as well as commercial interests limit data availability and, as a result, researchers can typically study a small number of datasets. In contrast, limited statistical characteristics of specific patient samples is much easier to share and may be available from previously published literature or focused collaborative efforts. Here, we propose a method that estimates model performance in external samples from their limited statistical characteristics. We search for weights that induce internal statistics that is similar to the external one; and that are closest to uniform. Then use model performance on the weighted internal sample as estimation for external one. We evaluate the proposed algorithm on simulated data and prediction model, as well as electronic medical record data for two risk models, predicting complications in ulcerative colitis patients and stroke in women diagnosed with atrial fibrillation. In the vast majority of cases, the estimated external performance is much closer to the actual one than the internal performance. Our proposed method may be an important building block in training robust models and detecting potential model failures in external environments.