To facilitate such learning, analytic tools with several key characteristics will be required. First, we need methods that ensure that the patient groups being compared are similar to one another, so that analysts can be sure they are actually studying the effects of care interventions rather than variation in the underlying severity of disease; propensity-score methods, which simultaneously account for many patient characteristics, have proved to robustly reduce confounding biases in studies using health care databases.

Second, most aspects of the analyses need to be automated without loss of validity, so that many research questions can be answered simultaneously and the number of matters investigated can grow as demand increases for quantifying the effectiveness of care. Extensions of propensityscore methods have been developed for automatically adapting to new data sources and reducing confounding. Third, once analyses have been automated, they should be able to be repeated in rapid cycles tied to data refreshes, which may occur as often as every 24 hours. Fourth, such software should be easy enough to use that users with little training can set up a learning system fairly quickly and avoid typical pitfalls of database studies that hamper causal interpretations of results—such as failures to designate the timing of the start of treatment and the onset of outcomes, to ensure comparison of similar patients, and to adjust robustly for confounding without adjusting for factors that lie on the causal pathway between exposure and outcome. Most important pitfalls can be avoided with fairly obvi-