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Improving the performance of the healthcare sector requires an understanding of the efficiency and effectiveness of care delivered by providers. Although this topic is of great interest to policymakers, researchers, and hospital managers, fair and scientific methods of measuring efficiency and effectiveness of care delivery have proven elusive. Through Data Envelopment Analysis (DEA), we make use of evidence from care delivered by emergency physicians, and shed light on scientific metrics that can gauge performance in terms of efficiency and effectiveness. We use these metrics along with Machine Learning techniques and Tobit analyses to identify the distinguishing behaviors of physicians who perform highly on these metrics. Our findings indicate a statistically significant positive relationship between a physician's effectiveness and efficiency scores suggesting that, contrary to conventional wisdom, high levels of effectiveness are not necessarily associated with low efficiency levels. In addition, we find that a physician's effectiveness is positively associated with his/her average contact-to-disposition time and negatively associated with his/her years of experience. We also find a statistically significant negative relationship between a physician's efficiency and his/her average MRI orders per patient visit. Furthermore, we find evidence of a peer effect of one physician upon another, which suggests an opportunity to improve system performance by taking physician characteristics into account when determining the set of physicians that should be scheduled during same shifts.