Does Medical School Training Relate to Practice? Evidence from Big Data.
On April 2nd, 2014, the Department of Health and Human Services (HHS) announced a historic policy in its effort to increase the transparency in the American healthcare system. The Center for Medicare and Medicaid Service (CMS) would publicly release a dataset containing information about the types of Medicare services, requested charges, and payments issued by providers across the country. In its release, HHS stated that the data would shed light on "Medicare fraud, waste, and abuse." While this is most certainly true, we believe that it can provide so much more. Beyond the purely financial aspects of procedure charges and payments, the procedures themselves may provide us with additional information, not only about the Medicare population, but also about the physicians themselves. The procedures a physician performs are for the most part not novel, but rather recommended, observed, and studied. However, whether a physician decides on advocating a procedure is somewhat discretionary. Some patients require a clear course of action, while others may benefit from a variety of options. This article poses the following question: How does a physician's past experience in medical school shape his or her practicing decisions? This article aims to open the analysis into how data, such as the CMS Medicare release, can help further our understanding of knowledge transfer and how experiences during education can shape a physician's decision's over the course of his or her career. This work begins with an evaluation into similarities between medical school charges, procedures, and payments. It then details how schools' procedure choices may link them in other, more interesting ways. Finally, the article includes a geographic analysis of how medical school procedure payments and charges are distributed nationally, highlighting potential deviations.
Department of Computer Science & Engineering, iCeNSA, University of Notre Dame , Notre Dame, Indiana.
Keith
K
Feldman
Department of Computer Science & Engineering, iCeNSA, University of Notre Dame , Notre Dame, Indiana.
Nitesh V
NV
Chawla
big data analytics; data acquisition and cleaning; data mining