Complexity in clinical workflows can lead to inefficiency in making diagnoses, ineffectiveness of treatment plans and uninformed management of healthcare organizations (HCOs). Traditional strategies to manage workflow complexity are based on measuring the gaps between workflows defined by HCO administrators and the actual processes followed by staff in the clinic. However, existing methods tend to neglect the influences of EMR systems on the utilization of workflows, which could be leveraged to optimize workflows facilitated through the EMR. In this paper, we introduce a framework to infer clinical workflows through the utilization of an EMR and show how such workflows roughly partition into four types according to their efficiency. Our framework infers workflows at several levels of granularity through data mining technologies. We study four months of EMR event logs from a large medical center, including 16,569 inpatient stays, and illustrate that over approximately 95% of workflows are efficient and that 80% of patients are on such workflows. At the same time, we show that the remaining 5% of workflows may be inefficient due to a variety of factors, such as complex patients.