Adjusting for large numbers of covariates ascertained from patients' health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. We developed and tested a "high dimensional propensity score" (hd-PS) algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model. Steps include (1) identifying data dimensions, eg, diagnoses, procedures, and medications; (2) empirically identifying candidate covariates; (3) assessing recurrence of codes; (4) prioritizing covariates; (5) selecting covariates for adjustment; (6) estimating the exposure propensity score; and (7) estimating an outcome model. In this presentation, Dr. Rassen will present the rationale for the hd-PS, illustrate how it can be applied to single studies and in safety surveillance systems, and show how hd-PS has been used in studies to date. He will also briefly discuss challenges with automated variable selection and with newly-marketed medications.