OBJECTIVES:  Develop and validate an algorithm to identify patients with hemophilia A in an administrative claims database. METHODS:  We first created a screening algorithm using diagnosis and treatment codes to identify potential hemophilia A patients from administrative claims data in the US HealthCore Integrated Research Database between 01/01/06 and 04/30/15. Medical records for a randomly selected subset of patients were reviewed to confirm case status. In this validation sample, we used lasso logistic regression with cross-validation to develop a predictive model using covariates in claims data to estimate the probability of being a confirmed hemophilia A case. RESULTS:  Using the screening algorithm, we identified an initial cohort of 2,252 patients with potential hemophilia A. Of 400 medical records reviewed, 248 (62%) patients were classified as hemophilia A cases, 131 (33%) were false positives, and 21 (5%) were of indeterminate status. The lasso regression model evaluated 36 potential covariates and identified several strong predictors of hemophilia A that were not included in the screening algorithm, including: ≥1 inpatient, outpatient or emergency room visit for hemophilia A; diagnosis after clotting factor level tests; diagnosis made by a hematologist and ≥1 hemophilia A diagnosis over 3 months. A probability threshold of ≥0.6 resulted in a PPV of 94.7% (95%CI: 92.0-97.5), sensitivity of 94.4% (95%CI: 91.5-97.2), and specificity of 90.1% (95%CI: 85.0-95.2) in the validation sample. We applied this model to the initial cohort to identify a refined cohort of 1,507 patients. The refined cohort was more likely to be male, be under the care of a hematologist, and have fewer comorbidities. CONCLUSIONS:  We developed and validated an algorithm to identify hemophilia A cases in an administrative claims database with high PPV, sensitivity and specificity. This algorithm uses widely available variables that can be applied in other claims databases.