Abstract
We live in a world that has been characterized by “big data.” Data that have been collected for years and even decades are now seen as important inputs to understanding current processes, predicting outcomes, and prescribing actions. This movement is so prominent that the term data mining has been supplanted by analytics to describe the broadly based and sophisticated methodologies that have been created and validated to allow these massive databases to be analyzed. Every major university has at least one course entitled “Analytics” and often degree programs to address the needs of business, engineering, psychology, and others. The NSQIP was initially introduced within the Veterans Affairs system in the mid-1990s as the first structured, risk-adjusted, prospective database of surgical outcomes. A private-sector version was developed and then implemented by the American College of Surgeons (ACS) and has rapidly evolved and matured into a national surgical outcomes database. This database now qualifies as “big data” and is available to investigators for analysis of specific surgical procedures and evaluation of surgical quality in hospitals and institutions. During the same years that the ACS NSQIP has “matured,” there has been considerable enhancement of the analytical techniques available to study large databases. These newer techniques were developed to address many of the limitations of the earlier methods and have been made possible because of the rapid expansion of the computing power of modern desktop computers and servers. The purpose of this review is to provide an overview of the analytical techniques currently available to enhance the ability of the reader, either clinician or investigator, to interpret and apply predictive modeling in surgical care. This review is not a treatise on statistics and is not intended to be an in-depth description of how to perform database queries or to use modern statistical software. The interested reader is referred to the focused references and
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