The aim of the learning healthcare system is to leverage data stored in the electronic health record (EHR) to gain insights into and improve healthcare delivery. Laboratory testing represents the largest source of routinely formatted data in the EHR and is therefore readily accessible for mining. In this presentation I will be describing a variety of practical applications of EHR data analytics relevant to clinical laboratory practice and patient care. These will include automatic generation of institutional reference ranges, synthesis of test panel results with machine learning models, fine-grained characterization of point-of-care instrument accuracy, effects of in-vitro hemolysis on central laboratory testing results, and the use of in-silico simulations to put the findings in context. Taking care to account for implicit confounders present in retrospective studies, a wealth of data exists in the EHR that can be unlocked to aid in day-to-day management of the clinical laboratory as well as guide healthcare policy-making in general.