The authors propose that the provision of state-of-the-art, effective, safe, and affordable health care requires medical school graduates not only to be competent practitioners and scientists but also to be policy makers and professional leaders. To meet this challenge in the era of big data and cloud computing, these graduates must be able to understand and critically interpret analyses of large, observational datasets from electronic health records, third-party claims files, surveys, and epidemiologic health datasets.
The authors contend that medical students need to be exposed to three components. First, students should be familiar with outcome metrics that not only are scientifically valid but also are robust, useful for the medical community, understandable to patients and relevant to their preferences and health goals, and persuasive to health administrators and policy decision makers. Next, students must interact with an inclusive set of analysts including biostatisticians, mathematical and computational statisticians, econometrists, psychometricians, epidemiologists, informaticians, and qualitative researchers. Last, students should learn in environments in which data analyses are not static with a “one-size-fits-all” solution but, rather, where mathematical and computer scientists provide new, innovative, and effective ways of solving predictable and commonplace data limitations such as missing data; make causal inferences from nonrandomized studies and/or those with selection biases; and estimate effect size when patient outcomes are heterogeneous and surveys have low response rates.